------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\mbeissin\Desktop\Stata files for book\Logfiles\chapter3.log
  log type:  text
 opened on:  25 Jan 2022, 20:13:17

. * ======================================================================
. * STATISTICAL RESULTS APPEARING IN CHAPTER 3, THE REVOLUTIONARY CITY
. * Results reported in Chapter 3 
. * Author: Mark R. Beissinger  
. * Date:  January 2022  
. * Princeton, NJ 
. * ======================================================================
. * BEFORE RUNNING, YOU MUST SET THE DEFAULT PATH FOR WHERE THE DATA
. *   FILES RESIDE
. * ======================================================================
. * The following datafiles were used in this chapter:
. *   Panel data for revolutionary episodes--revspredictbycntryyr.dta
. *   Multiple imputation panel data--revspredictbycntryyrmi.dta
. * ======================================================================
. * Output produced:  Logfiles\chapter3.log
. * ======================================================================
. 
. use revspredictbycntryyr.dta

. 
. * ======================================================================
. * BIVARIATE RELATIONS FOR URBAN CIVIC AND SOCIAL REVOLUTIONARY EPISODES
. *   (FIGURES 3.1 TO 3.5)
. * ======================================================================
. * Graphs of marginal effects--results were loaded in Excel spreadsheets
. * Only independent states included to keep samples analogous.
. 
. * Figure 3.1
. xtcloglog urbancivicny  polityl c.polityl#c.polityl c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform 
> nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,328
Group variable: cowcode                         Number of groups  =        159

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
                                                              avg =       71.2
                                                              max =        115

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(5)      =      45.58
Log pseudolikelihood  = -286.70449              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 159 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
           urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
                polityl |   .9097204   .0249435    -3.45   0.001     .8621224    .9599464
                        |
    c.polityl#c.polityl |   .9836101   .0049711    -3.27   0.001     .9739151    .9934016
                        |
                  time1 |   .9544682   .0784993    -0.57   0.571     .8123725    1.121418
                        |
        c.time1#c.time1 |   1.001478    .001393     1.06   0.288     .9987515    1.004212
                        |
c.time1#c.time1#c.time1 |   .9999932   6.95e-06    -0.98   0.325     .9999795    1.000007
                        |
                  _cons |   .0005532   .0009291    -4.47   0.000     .0000206    .0148737
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.8144803   .8784393                      -2.53619    .9072291
------------------------+----------------------------------------------------------------
                sigma_u |   .6654844   .2922938                      .2813672    1.573991
                    rho |   .2121222   .1468104                      .0459181    .6009749
-----------------------------------------------------------------------------------------

. margins, at(polityl=-10) at(polityl=-9) at(polityl=-8) at(polityl=-7) at(polityl=-6) at(polityl=-5) at(polityl=-
> 4) at(polityl=-3) at(polityl=-2) at(polityl=-1) at(polityl=0) at(polityl=1) at(polityl=2) at(polityl=3) at(polit
> yl=4) at(polityl=5) at(polityl=6) at(polityl=7) at(polityl=8) at(polityl=9) at(polityl=10) predict(pr)

Predictive margins                              Number of obs     =     11,328
Model VCE    : Robust

Expression   : Pr(urbancivicny=1), predict(pr)

1._at        : polityl         =         -10

2._at        : polityl         =          -9

3._at        : polityl         =          -8

4._at        : polityl         =          -7

5._at        : polityl         =          -6

6._at        : polityl         =          -5

7._at        : polityl         =          -4

8._at        : polityl         =          -3

9._at        : polityl         =          -2

10._at       : polityl         =          -1

11._at       : polityl         =           0

12._at       : polityl         =           1

13._at       : polityl         =           2

14._at       : polityl         =           3

15._at       : polityl         =           4

16._at       : polityl         =           5

17._at       : polityl         =           6

18._at       : polityl         =           7

19._at       : polityl         =           8

20._at       : polityl         =           9

21._at       : polityl         =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0044996   .0015949     2.82   0.005     .0013737    .0076255
          2  |   .0055937   .0015931     3.51   0.000     .0024713    .0087161
          3  |   .0067273   .0015741     4.27   0.000     .0036421    .0098125
          4  |    .007828   .0016001     4.89   0.000     .0046919    .0109641
          5  |   .0088142   .0017201     5.12   0.000      .005443    .0121855
          6  |    .009605   .0019255     4.99   0.000      .005831    .0133789
          7  |   .0101306   .0021558     4.70   0.000     .0059053    .0143559
          8  |   .0103428   .0023397     4.42   0.000      .005757    .0149286
          9  |   .0102215   .0024246     4.22   0.000     .0054694    .0149736
         10  |   .0097782    .002386     4.10   0.000     .0051018    .0144546
         11  |    .009054   .0022279     4.06   0.000     .0046873    .0134206
         12  |   .0081136   .0019779     4.10   0.000     .0042371    .0119901
         13  |   .0070361   .0016779     4.19   0.000     .0037473    .0103248
         14  |   .0059037   .0013747     4.29   0.000     .0032094    .0085981
         15  |   .0047924   .0011077     4.33   0.000     .0026213    .0069635
         16  |   .0037633   .0008992     4.19   0.000      .002001    .0055256
         17  |   .0028586   .0007478     3.82   0.000     .0013929    .0043243
         18  |   .0021003   .0006348     3.31   0.001      .000856    .0033445
         19  |   .0014926   .0005389     2.77   0.006     .0004363    .0025488
         20  |    .001026   .0004479     2.29   0.022     .0001482    .0019038
         21  |   .0006822   .0003595     1.90   0.058    -.0000223    .0013868
------------------------------------------------------------------------------

. xtcloglog leftistny polityl c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,328
Group variable: cowcode                         Number of groups  =        159

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
                                                              avg =       71.2
                                                              max =        115

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      32.63
Log pseudolikelihood  = -368.40781              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 159 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
              leftistny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
                polityl |   1.010763   .0206784     0.52   0.601     .9710356    1.052115
                  time1 |   .9587571   .0313343    -1.29   0.198     .8992686    1.022181
                        |
        c.time1#c.time1 |   1.001584   .0007652     2.07   0.038     1.000085    1.003085
                        |
c.time1#c.time1#c.time1 |    .999986   4.92e-06    -2.85   0.004     .9999763    .9999956
                        |
                  _cons |    .007374   .0033865   -10.69   0.000     .0029977    .0181392
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.7080202   .6085355                     -1.900728    .4846875
------------------------+----------------------------------------------------------------
                sigma_u |   .7018679   .2135558                      .3866003    1.274232
                    rho |   .2304591   .1079224                      .0832926    .4967468
-----------------------------------------------------------------------------------------

. margins, at(polityl=-10) at(polityl=-9) at(polityl=-8) at(polityl=-7) at(polityl=-6) at(polityl=-5) at(polityl=-
> 4) at(polityl=-3) at(polityl=-2) at(polityl=-1) at(polityl=0) at(polityl=1) at(polityl=2) at(polityl=3) at(polit
> yl=4) at(polityl=5) at(polityl=6) at(polityl=7) at(polityl=8) at(polityl=9) at(polityl=10) predict(pr)

Predictive margins                              Number of obs     =     11,328
Model VCE    : Robust

Expression   : Pr(leftistny=1), predict(pr)

1._at        : polityl         =         -10

2._at        : polityl         =          -9

3._at        : polityl         =          -8

4._at        : polityl         =          -7

5._at        : polityl         =          -6

6._at        : polityl         =          -5

7._at        : polityl         =          -4

8._at        : polityl         =          -3

9._at        : polityl         =          -2

10._at       : polityl         =          -1

11._at       : polityl         =           0

12._at       : polityl         =           1

13._at       : polityl         =           2

14._at       : polityl         =           3

15._at       : polityl         =           4

16._at       : polityl         =           5

17._at       : polityl         =           6

18._at       : polityl         =           7

19._at       : polityl         =           8

20._at       : polityl         =           9

21._at       : polityl         =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0049575   .0010203     4.86   0.000     .0029577    .0069573
          2  |   .0050105   .0009587     5.23   0.000     .0031315    .0068896
          3  |   .0050641   .0009015     5.62   0.000     .0032972     .006831
          4  |   .0051183   .0008503     6.02   0.000     .0034518    .0067848
          5  |    .005173   .0008069     6.41   0.000     .0035915    .0067546
          6  |   .0052284   .0007736     6.76   0.000     .0037122    .0067446
          7  |   .0052843   .0007525     7.02   0.000     .0038094    .0067592
          8  |   .0053408   .0007457     7.16   0.000     .0038793    .0068023
          9  |   .0053979   .0007544     7.16   0.000     .0039193    .0068765
         10  |   .0054556   .0007791     7.00   0.000     .0039285    .0069827
         11  |   .0055139   .0008194     6.73   0.000     .0039079      .00712
         12  |   .0055729   .0008741     6.38   0.000     .0038597     .007286
         13  |   .0056324   .0009414     5.98   0.000     .0037873    .0074776
         14  |   .0056927   .0010199     5.58   0.000     .0036937    .0076916
         15  |   .0057535    .001108     5.19   0.000     .0035819    .0079251
         16  |    .005815   .0012043     4.83   0.000     .0034546    .0081754
         17  |   .0058771   .0013079     4.49   0.000     .0033137    .0084406
         18  |   .0059399   .0014179     4.19   0.000      .003161    .0087189
         19  |   .0060034   .0015335     3.91   0.000     .0029977    .0090091
         20  |   .0060675   .0016544     3.67   0.000     .0028249    .0093102
         21  |   .0061323   .0017801     3.44   0.001     .0026434    .0096213
------------------------------------------------------------------------------

. 
. * On relationship of non-democratic regime-types to probability of onset for urban civic and social revolutionar
> y episodes (Geddes data)
. xtcloglog urbancivicny  gedpartyautoc gedmilautoc gedmonautoc gedpersautoc c.time1##c.time1##c.time1 if indstate
> ==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =      7,699
Group variable: cowcode                         Number of groups  =        148

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         17
                                                              avg =       52.0
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(7)      =     101.45
Log pseudolikelihood  = -226.49849              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 148 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
           urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
          gedpartyautoc |   4.185325   2.137913     2.80   0.005     1.537891    11.39024
            gedmilautoc |   16.82953   8.762122     5.42   0.000     6.066006    46.69188
            gedmonautoc |   3.093933   3.643171     0.96   0.337     .3077538    31.10416
           gedpersautoc |   6.316402    2.95918     3.93   0.000     2.521675     15.8216
                  time1 |   .5916528   .3517203    -0.88   0.377     .1845238    1.897061
                        |
        c.time1#c.time1 |   1.007509   .0076581     0.98   0.325     .9926108    1.022631
                        |
c.time1#c.time1#c.time1 |   .9999689   .0000316    -0.98   0.325     .9999069    1.000031
                        |
                  _cons |   16.45375   248.4718     0.19   0.853     2.30e-12    1.18e+14
------------------------+----------------------------------------------------------------
               /lnsig2u |  -1.057451     1.3848                      -3.77161    1.656708
------------------------+----------------------------------------------------------------
                sigma_u |   .5893556   .4080699                      .1517069    2.289547
                    rho |   .1743435    .199339                      .0137984    .7611523
-----------------------------------------------------------------------------------------

. xtcloglog leftistny  gedpartyautoc gedmilautoc gedmonautoc gedpersautoc c.time1##c.time1##c.time1 if indstate==1
> , vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =      7,699
Group variable: cowcode                         Number of groups  =        148

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         17
                                                              avg =       52.0
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(7)      =      15.57
Log pseudolikelihood  = -236.49565              Prob > chi2       =     0.0293

                                         (Std. Err. adjusted for 148 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
              leftistny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
          gedpartyautoc |   .5607227   .2773734    -1.17   0.242     .2126587    1.478472
            gedmilautoc |   1.244463   .6393433     0.43   0.670       .45465    3.406333
            gedmonautoc |   .9610481    .562433    -0.07   0.946     .3052091    3.026166
           gedpersautoc |   1.606716   .7111051     1.07   0.284     .6748569    3.825308
                  time1 |   .5108565   .5117237    -0.67   0.503      .071722    3.638693
                        |
        c.time1#c.time1 |   1.011495   .0140998     0.82   0.412      .984234    1.039511
                        |
c.time1#c.time1#c.time1 |    .999937   .0000634    -0.99   0.321     .9998127    1.000061
                        |
                  _cons |   2540.501   59795.73     0.33   0.739     2.35e-17    2.75e+23
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.7267684     .83941                     -2.371982     .918445
------------------------+----------------------------------------------------------------
                sigma_u |   .6953192    .291829                      .3054434    1.582843
                    rho |    .227151   .1473613                      .0536728    .6036614
-----------------------------------------------------------------------------------------

. 
. * Figure 3.2
. * No statistically significant relationship between yrsincleaderinpower and social revolutionary episodes (vario
> us polynomial forms tested)
. xtcloglog urbancivicny yrsincleaderinpower c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,661
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
                                                              avg =       71.1
                                                              max =        115

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      32.16
Log pseudolikelihood  = -298.85435              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 164 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
           urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
    yrsincleaderinpower |   1.059296     .01397     4.37   0.000     1.032266    1.087034
                  time1 |   .9782771   .0773353    -0.28   0.781     .8378615    1.142225
                        |
        c.time1#c.time1 |    1.00111   .0013404     0.83   0.407     .9984868    1.003741
                        |
c.time1#c.time1#c.time1 |   .9999944   6.73e-06    -0.83   0.408     .9999812    1.000008
                        |
                  _cons |   .0001371   .0002396    -5.09   0.000     4.46e-06    .0042146
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.5252071   .6315522                     -1.763027    .7126124
------------------------+----------------------------------------------------------------
                sigma_u |   .7690467   .2428466                      .4141557    1.428045
                    rho |   .2644615   .1228505                      .0944282    .5535222
-----------------------------------------------------------------------------------------

. margins, at(yrsincleaderinpower=(0(1)35)) predict(pr)

Predictive margins                              Number of obs     =     11,661
Model VCE    : Robust

Expression   : Pr(urbancivicny=1), predict(pr)

1._at        : yrsinclead~r    =           0

2._at        : yrsinclead~r    =           1

3._at        : yrsinclead~r    =           2

4._at        : yrsinclead~r    =           3

5._at        : yrsinclead~r    =           4

6._at        : yrsinclead~r    =           5

7._at        : yrsinclead~r    =           6

8._at        : yrsinclead~r    =           7

9._at        : yrsinclead~r    =           8

10._at       : yrsinclead~r    =           9

11._at       : yrsinclead~r    =          10

12._at       : yrsinclead~r    =          11

13._at       : yrsinclead~r    =          12

14._at       : yrsinclead~r    =          13

15._at       : yrsinclead~r    =          14

16._at       : yrsinclead~r    =          15

17._at       : yrsinclead~r    =          16

18._at       : yrsinclead~r    =          17

19._at       : yrsinclead~r    =          18

20._at       : yrsinclead~r    =          19

21._at       : yrsinclead~r    =          20

22._at       : yrsinclead~r    =          21

23._at       : yrsinclead~r    =          22

24._at       : yrsinclead~r    =          23

25._at       : yrsinclead~r    =          24

26._at       : yrsinclead~r    =          25

27._at       : yrsinclead~r    =          26

28._at       : yrsinclead~r    =          27

29._at       : yrsinclead~r    =          28

30._at       : yrsinclead~r    =          29

31._at       : yrsinclead~r    =          30

32._at       : yrsinclead~r    =          31

33._at       : yrsinclead~r    =          32

34._at       : yrsinclead~r    =          33

35._at       : yrsinclead~r    =          34

36._at       : yrsinclead~r    =          35

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0026626    .000528     5.04   0.000     .0016279    .0036974
          2  |   .0028198   .0005362     5.26   0.000     .0017688    .0038708
          3  |   .0029862   .0005454     5.48   0.000     .0019173    .0040552
          4  |   .0031624   .0005558     5.69   0.000      .002073    .0042518
          5  |   .0033489    .000568     5.90   0.000     .0022356    .0044623
          6  |   .0035464   .0005827     6.09   0.000     .0024044    .0046884
          7  |   .0037554   .0006004     6.26   0.000     .0025787    .0049321
          8  |   .0039767    .000622     6.39   0.000     .0027577    .0051957
          9  |   .0042109   .0006484     6.49   0.000     .0029402    .0054817
         10  |   .0044589   .0006805     6.55   0.000     .0031251    .0057926
         11  |   .0047213   .0007193     6.56   0.000     .0033114    .0061311
         12  |    .004999   .0007659     6.53   0.000     .0034979    .0065001
         13  |   .0052929   .0008211     6.45   0.000     .0036836    .0069023
         14  |    .005604    .000886     6.33   0.000     .0038675    .0073405
         15  |   .0059332   .0009614     6.17   0.000     .0040489    .0078174
         16  |   .0062815   .0010482     5.99   0.000     .0042271    .0083359
         17  |   .0066501   .0011473     5.80   0.000     .0044015    .0088987
         18  |     .00704   .0012595     5.59   0.000     .0045714    .0095086
         19  |   .0074526   .0013858     5.38   0.000     .0047365    .0101686
         20  |    .007889    .001527     5.17   0.000     .0048961    .0108818
         21  |   .0083506   .0016841     4.96   0.000     .0050498    .0116514
         22  |   .0088389   .0018581     4.76   0.000      .005197    .0124808
         23  |   .0093552   .0020502     4.56   0.000     .0053368    .0133736
         24  |   .0099013   .0022616     4.38   0.000     .0054688    .0143339
         25  |   .0104788   .0024933     4.20   0.000     .0055919    .0153656
         26  |   .0110893    .002747     4.04   0.000     .0057053    .0164733
         27  |   .0117347   .0030239     3.88   0.000      .005808    .0176615
         28  |    .012417   .0033257     3.73   0.000     .0058988    .0189353
         29  |   .0131381    .003654     3.60   0.000     .0059765    .0202998
         30  |   .0139002   .0040105     3.47   0.001     .0060397    .0217607
         31  |   .0147055   .0043972     3.34   0.001     .0060871    .0233239
         32  |   .0155563   .0048161     3.23   0.001     .0061169    .0249957
         33  |    .016455   .0052692     3.12   0.002     .0061276    .0267824
         34  |   .0174043   .0057588     3.02   0.003     .0061173    .0286912
         35  |   .0184067   .0062872     2.93   0.003     .0060841    .0307293
         36  |   .0194651   .0068569     2.84   0.005     .0060259    .0329043
------------------------------------------------------------------------------

. xtcloglog leftistny yrsincleaderinpower c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,661
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
                                                              avg =       71.1
                                                              max =        115

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      29.89
Log pseudolikelihood  = -379.30202              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 164 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
              leftistny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
    yrsincleaderinpower |    1.00156   .0155939     0.10   0.920     .9714583    1.032595
                  time1 |      .9657    .030468    -1.11   0.269     .9077926    1.027301
                        |
        c.time1#c.time1 |   1.001435   .0007522     1.91   0.056      .999962    1.002911
                        |
c.time1#c.time1#c.time1 |   .9999868   4.89e-06    -2.71   0.007     .9999772    .9999964
                        |
                  _cons |   .0068245   .0028516   -11.94   0.000     .0030088    .0154789
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.7040494   .6019727                     -1.883894    .4757955
------------------------+----------------------------------------------------------------
                sigma_u |   .7032627   .2116725                       .389868    1.268579
                    rho |   .2311641    .106987                       .084587     .494524
-----------------------------------------------------------------------------------------

. margins, at(yrsincleaderinpower=(0(1)35)) predict(pr)

Predictive margins                              Number of obs     =     11,661
Model VCE    : Robust

Expression   : Pr(leftistny=1), predict(pr)

1._at        : yrsinclead~r    =           0

2._at        : yrsinclead~r    =           1

3._at        : yrsinclead~r    =           2

4._at        : yrsinclead~r    =           3

5._at        : yrsinclead~r    =           4

6._at        : yrsinclead~r    =           5

7._at        : yrsinclead~r    =           6

8._at        : yrsinclead~r    =           7

9._at        : yrsinclead~r    =           8

10._at       : yrsinclead~r    =           9

11._at       : yrsinclead~r    =          10

12._at       : yrsinclead~r    =          11

13._at       : yrsinclead~r    =          12

14._at       : yrsinclead~r    =          13

15._at       : yrsinclead~r    =          14

16._at       : yrsinclead~r    =          15

17._at       : yrsinclead~r    =          16

18._at       : yrsinclead~r    =          17

19._at       : yrsinclead~r    =          18

20._at       : yrsinclead~r    =          19

21._at       : yrsinclead~r    =          20

22._at       : yrsinclead~r    =          21

23._at       : yrsinclead~r    =          22

24._at       : yrsinclead~r    =          23

25._at       : yrsinclead~r    =          24

26._at       : yrsinclead~r    =          25

27._at       : yrsinclead~r    =          26

28._at       : yrsinclead~r    =          27

29._at       : yrsinclead~r    =          28

30._at       : yrsinclead~r    =          29

31._at       : yrsinclead~r    =          30

32._at       : yrsinclead~r    =          31

33._at       : yrsinclead~r    =          32

34._at       : yrsinclead~r    =          33

35._at       : yrsinclead~r    =          34

36._at       : yrsinclead~r    =          35

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0054374   .0008945     6.08   0.000     .0036842    .0071907
          2  |   .0054459   .0008571     6.35   0.000     .0037659    .0071258
          3  |   .0054543   .0008264     6.60   0.000     .0038345    .0070741
          4  |   .0054627   .0008033     6.80   0.000     .0038884    .0070371
          5  |   .0054712   .0007884     6.94   0.000      .003926    .0070165
          6  |   .0054797   .0007824     7.00   0.000     .0039462    .0070132
          7  |   .0054882   .0007855     6.99   0.000     .0039486    .0070278
          8  |   .0054967   .0007978     6.89   0.000     .0039331    .0070603
          9  |   .0055052   .0008188     6.72   0.000     .0039004      .00711
         10  |   .0055137    .000848     6.50   0.000     .0038516    .0071759
         11  |   .0055223   .0008847     6.24   0.000     .0037882    .0072564
         12  |   .0055308   .0009281     5.96   0.000     .0037118    .0073499
         13  |   .0055394   .0009773     5.67   0.000     .0036239     .007455
         14  |    .005548   .0010316     5.38   0.000      .003526      .00757
         15  |   .0055566   .0010903     5.10   0.000     .0034196    .0076935
         16  |   .0055652   .0011527     4.83   0.000     .0033059    .0078246
         17  |   .0055738   .0012184     4.57   0.000     .0031857    .0079619
         18  |   .0055825    .001287     4.34   0.000     .0030601    .0081048
         19  |   .0055911   .0013579     4.12   0.000     .0029297    .0082525
         20  |   .0055998    .001431     3.91   0.000     .0027952    .0084044
         21  |   .0056085   .0015059     3.72   0.000      .002657    .0085599
         22  |   .0056171   .0015824     3.55   0.000     .0025156    .0087187
         23  |   .0056258   .0016605     3.39   0.001     .0023714    .0088803
         24  |   .0056346   .0017398     3.24   0.001     .0022246    .0090445
         25  |   .0056433   .0018203     3.10   0.002     .0020756     .009211
         26  |    .005652   .0019019     2.97   0.003     .0019244    .0093797
         27  |   .0056608   .0019844     2.85   0.004     .0017714    .0095502
         28  |   .0056696   .0020679     2.74   0.006     .0016166    .0097225
         29  |   .0056784   .0021521     2.64   0.008     .0014603    .0098964
         30  |   .0056871   .0022371     2.54   0.011     .0013025    .0100718
         31  |    .005696   .0023229     2.45   0.014     .0011432    .0102487
         32  |   .0057048   .0024093     2.37   0.018     .0009827    .0104269
         33  |   .0057136   .0024963     2.29   0.022      .000821    .0106063
         34  |   .0057225   .0025839     2.21   0.027     .0006581    .0107869
         35  |   .0057313   .0026721     2.14   0.032     .0004941    .0109686
         36  |   .0057402   .0027608     2.08   0.038     .0003291    .0111514
------------------------------------------------------------------------------

. 
. * V-Dem executive corruption measure
. xtcloglog urbancivicny v2x_execorr c.time1##c.time1##c.time1 if indstate==1,  eform nolog vce(robust)

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,580
Group variable: cowcode                         Number of groups  =        161

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         21
                                                              avg =       71.9
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      29.38
Log pseudolikelihood  = -297.91275              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 161 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
           urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
            v2x_execorr |   8.632152   5.003908     3.72   0.000     2.771356    26.88721
                  time1 |   .9615505   .0831322    -0.45   0.650     .8116714    1.139106
                        |
        c.time1#c.time1 |   1.001318   .0013954     0.95   0.344     .9985872    1.004057
                        |
c.time1#c.time1#c.time1 |   .9999936   6.88e-06    -0.93   0.351     .9999801    1.000007
                        |
                  _cons |   .0001329     .00027    -4.39   0.000     2.47e-06    .0071319
------------------------+----------------------------------------------------------------
               /lnsig2u |  -2.532605    2.80237                     -8.025149     2.95994
------------------------+----------------------------------------------------------------
                sigma_u |   .2818719   .3949547                      .0180868    4.392813
                    rho |   .0460754   .1231711                      .0001988    .9214519
-----------------------------------------------------------------------------------------

. margins, atmeans at(v2x_execorr=(0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0)) predict(pr)

Adjusted predictions                            Number of obs     =     11,580
Model VCE    : Robust

Expression   : Pr(urbancivicny=1), predict(pr)

1._at        : v2x_execorr     =           0
               time1           =    71.57055 (mean)

2._at        : v2x_execorr     =          .1
               time1           =    71.57055 (mean)

3._at        : v2x_execorr     =          .2
               time1           =    71.57055 (mean)

4._at        : v2x_execorr     =          .3
               time1           =    71.57055 (mean)

5._at        : v2x_execorr     =          .4
               time1           =    71.57055 (mean)

6._at        : v2x_execorr     =          .5
               time1           =    71.57055 (mean)

7._at        : v2x_execorr     =          .6
               time1           =    71.57055 (mean)

8._at        : v2x_execorr     =          .7
               time1           =    71.57055 (mean)

9._at        : v2x_execorr     =          .8
               time1           =    71.57055 (mean)

10._at       : v2x_execorr     =          .9
               time1           =    71.57055 (mean)

11._at       : v2x_execorr     =           1
               time1           =    71.57055 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0006766   .0003347     2.02   0.043     .0000206    .0013325
          2  |   .0008392   .0003757     2.23   0.025     .0001029    .0015756
          3  |    .001041   .0004201     2.48   0.013     .0002176    .0018644
          4  |   .0012912   .0004691     2.75   0.006     .0003717    .0022107
          5  |   .0016015    .000526     3.04   0.002     .0005705    .0026326
          6  |   .0019864   .0005975     3.32   0.001     .0008153    .0031574
          7  |   .0024635   .0006958     3.54   0.000     .0010998    .0038273
          8  |   .0030552    .000841     3.63   0.000     .0014069    .0047034
          9  |   .0037885   .0010611     3.57   0.000     .0017088    .0058683
         10  |   .0046975   .0013918     3.38   0.001     .0019696    .0074254
         11  |   .0058239   .0018759     3.10   0.002     .0021472    .0095006
------------------------------------------------------------------------------

. * Comparing bottom 10 percent, the mean, the top 90-99 percent
. sum v2x_execorr, detail

         V-Dem measure of executive corruption, t-1
-------------------------------------------------------------
      Percentiles      Smallest
 1%      .010446        .009338
 5%      .030093        .009338
10%      .061457        .009338       Obs              15,611
25%      .192158        .009338       Sum of Wgt.      15,611

50%      .457809                      Mean           .4701649
                        Largest       Std. Dev.      .2991762
75%       .74321        .973844
90%      .877365        .973844       Variance       .0895064
95%      .918347        .973844       Skewness       .0295046
99%      .962508        .977558       Kurtosis       1.657601

. margins, at(v2x_execorr=(.060949 .4693745 .877365 .962508)) predict(pr)

Predictive margins                              Number of obs     =     11,580
Model VCE    : Robust

Expression   : Pr(urbancivicny=1), predict(pr)

1._at        : v2x_execorr     =     .060949

2._at        : v2x_execorr     =    .4693745

3._at        : v2x_execorr     =     .877365

4._at        : v2x_execorr     =     .962508

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0013936   .0005088     2.74   0.006     .0003964    .0023907
          2  |   .0033548   .0005772     5.81   0.000     .0022236     .004486
          3  |   .0080476   .0015519     5.19   0.000      .005006    .0110893
          4  |   .0096541   .0022118     4.36   0.000      .005319    .0139891
------------------------------------------------------------------------------

. xtcloglog leftistny v2x_execorr c.time1##c.time1##c.time1 if indstate==1,  eform nolog vce(robust)

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,580
Group variable: cowcode                         Number of groups  =        161

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         21
                                                              avg =       71.9
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      30.42
Log pseudolikelihood  = -378.06417              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 161 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
              leftistny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
            v2x_execorr |   1.786318   .7119443     1.46   0.145     .8179168    3.901294
                  time1 |   .9557295   .0327221    -1.32   0.186     .8936998    1.022064
                        |
        c.time1#c.time1 |   1.001647   .0007747     2.13   0.033      1.00013    1.003166
                        |
c.time1#c.time1#c.time1 |   .9999855   4.95e-06    -2.93   0.003     .9999758    .9999952
                        |
                  _cons |   .0060524   .0035095    -8.81   0.000     .0019425     .018858
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.7424384    .670923                     -2.057423    .5725464
------------------------+----------------------------------------------------------------
                sigma_u |   .6898927   .2314324                      .3574672    1.331456
                    rho |   .2244118   .1167749                       .072083    .5187028
-----------------------------------------------------------------------------------------

. margins, at(v2x_execorr=(0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0)) predict(pr)

Predictive margins                              Number of obs     =     11,580
Model VCE    : Robust

Expression   : Pr(leftistny=1), predict(pr)

1._at        : v2x_execorr     =           0

2._at        : v2x_execorr     =          .1

3._at        : v2x_execorr     =          .2

4._at        : v2x_execorr     =          .3

5._at        : v2x_execorr     =          .4

6._at        : v2x_execorr     =          .5

7._at        : v2x_execorr     =          .6

8._at        : v2x_execorr     =          .7

9._at        : v2x_execorr     =          .8

10._at       : v2x_execorr     =          .9

11._at       : v2x_execorr     =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .004177   .0011282     3.70   0.000     .0019657    .0063883
          2  |   .0044251   .0010483     4.22   0.000     .0023706    .0064797
          3  |    .004688   .0009657     4.85   0.000     .0027951    .0065808
          4  |   .0049663   .0008873     5.60   0.000     .0032273    .0067053
          5  |   .0052611   .0008244     6.38   0.000     .0036454    .0068768
          6  |   .0055732   .0007947     7.01   0.000     .0040156    .0071308
          7  |   .0059037   .0008193     7.21   0.000      .004298    .0075094
          8  |   .0062537    .000913     6.85   0.000     .0044642    .0080432
          9  |   .0066243   .0010784     6.14   0.000     .0045107    .0087378
         10  |   .0070166    .001309     5.36   0.000      .004451    .0095822
         11  |    .007432   .0015977     4.65   0.000     .0043005    .0105634
------------------------------------------------------------------------------

. 
. * Figure 3.3
. xtcloglog urbancivicny c.gdppcthl##c.gdppcthl c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,042
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
                                                              avg =       67.3
                                                              max =        115

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(5)      =      44.40
Log pseudolikelihood  = -298.49629              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 164 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
           urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
               gdppcthl |   1.352199   .1456203     2.80   0.005     1.094898    1.669967
                        |
  c.gdppcthl#c.gdppcthl |   .9748752   .0073925    -3.36   0.001     .9604932    .9894725
                        |
                  time1 |   .9331617   .0731267    -0.88   0.377     .8003002     1.08808
                        |
        c.time1#c.time1 |   1.001773   .0013091     1.36   0.175     .9992101    1.004342
                        |
c.time1#c.time1#c.time1 |   .9999917   6.56e-06    -1.27   0.205     .9999788    1.000005
                        |
                  _cons |   .0004842   .0008109    -4.56   0.000     .0000182    .0128982
------------------------+----------------------------------------------------------------
               /lnsig2u |  -3.246525      6.543                     -16.07057    9.577519
------------------------+----------------------------------------------------------------
                sigma_u |   .1972541   .6453169                      .0003238    120.1522
                    rho |   .0231074   .1476979                      6.38e-08    .9998861
-----------------------------------------------------------------------------------------

. margins, at(gdppcthl=(0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15)) predict(pr)

Predictive margins                              Number of obs     =     11,042
Model VCE    : Robust

Expression   : Pr(urbancivicny=1), predict(pr)

1._at        : gdppcthl        =           0

2._at        : gdppcthl        =           1

3._at        : gdppcthl        =           2

4._at        : gdppcthl        =           3

5._at        : gdppcthl        =           4

6._at        : gdppcthl        =           5

7._at        : gdppcthl        =           6

8._at        : gdppcthl        =           7

9._at        : gdppcthl        =           8

10._at       : gdppcthl        =           9

11._at       : gdppcthl        =          10

12._at       : gdppcthl        =          11

13._at       : gdppcthl        =          12

14._at       : gdppcthl        =          13

15._at       : gdppcthl        =          14

16._at       : gdppcthl        =          15

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0031041   .0009816     3.16   0.002     .0011802     .005028
          2  |   .0040882   .0009596     4.26   0.000     .0022075     .005969
          3  |    .005117   .0009107     5.62   0.000     .0033322    .0069019
          4  |   .0060873     .00092     6.62   0.000      .004284    .0078905
          5  |   .0068832   .0010464     6.58   0.000     .0048323    .0089341
          6  |   .0073989   .0012466     5.94   0.000     .0049557    .0098422
          7  |   .0075611   .0014342     5.27   0.000     .0047502     .010372
          8  |   .0073459   .0015509     4.74   0.000     .0043063    .0103856
          9  |   .0067849   .0015799     4.29   0.000     .0036885    .0098814
         10  |   .0059572   .0015343     3.88   0.000     .0029501    .0089644
         11  |   .0049717   .0014384     3.46   0.001     .0021525    .0077909
         12  |   .0039435   .0013109     3.01   0.003     .0013742    .0065128
         13  |   .0029726   .0011601     2.56   0.010     .0006989    .0052462
         14  |   .0021293   .0009897     2.15   0.031     .0001895     .004069
         15  |   .0014493    .000807     1.80   0.073    -.0001324     .003031
         16  |   .0009374   .0006247     1.50   0.133    -.0002869    .0021617
------------------------------------------------------------------------------

. xtcloglog leftistny gdppcthl c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,042
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
                                                              avg =       67.3
                                                              max =        115

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      41.83
Log pseudolikelihood  = -371.60028              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 164 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
              leftistny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
               gdppcthl |   .8990926   .0422104    -2.27   0.023     .8200539    .9857493
                  time1 |   .9601783   .0310018    -1.26   0.208     .9012985    1.022905
                        |
        c.time1#c.time1 |   1.001404   .0007465     1.88   0.060     .9999422    1.002868
                        |
c.time1#c.time1#c.time1 |   .9999876   4.75e-06    -2.62   0.009     .9999783    .9999969
                        |
                  _cons |   .0140395   .0064059    -9.35   0.000     .0057407    .0343348
------------------------+----------------------------------------------------------------
               /lnsig2u |  -1.205998   .8547113                     -2.881202     .469205
------------------------+----------------------------------------------------------------
                sigma_u |   .5471681   .2338354                      .2367854    1.264406
                    rho |   .1539828    .111345                      .0329614    .4928767
-----------------------------------------------------------------------------------------

. margins, at(gdppcthl=(0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15)) predict(pr)

Predictive margins                              Number of obs     =     11,042
Model VCE    : Robust

Expression   : Pr(leftistny=1), predict(pr)

1._at        : gdppcthl        =           0

2._at        : gdppcthl        =           1

3._at        : gdppcthl        =           2

4._at        : gdppcthl        =           3

5._at        : gdppcthl        =           4

6._at        : gdppcthl        =           5

7._at        : gdppcthl        =           6

8._at        : gdppcthl        =           7

9._at        : gdppcthl        =           8

10._at       : gdppcthl        =           9

11._at       : gdppcthl        =          10

12._at       : gdppcthl        =          11

13._at       : gdppcthl        =          12

14._at       : gdppcthl        =          13

15._at       : gdppcthl        =          14

16._at       : gdppcthl        =          15

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0078519   .0014818     5.30   0.000     .0049476    .0107563
          2  |   .0070653   .0011331     6.24   0.000     .0048445    .0092861
          3  |    .006357   .0009021     7.05   0.000      .004589     .008125
          4  |   .0057193    .000786     7.28   0.000     .0041787    .0072599
          5  |   .0051452   .0007631     6.74   0.000     .0036495    .0066408
          6  |   .0046284   .0007946     5.82   0.000      .003071    .0061858
          7  |   .0041634   .0008464     4.92   0.000     .0025044    .0058223
          8  |   .0037449   .0008986     4.17   0.000     .0019836    .0055061
          9  |   .0033683   .0009421     3.58   0.000     .0015218    .0052147
         10  |   .0030294   .0009736     3.11   0.002     .0011212    .0049376
         11  |   .0027246   .0009927     2.74   0.006      .000779    .0046702
         12  |   .0024504   .0010002     2.45   0.014       .00049    .0044107
         13  |   .0022036   .0009975     2.21   0.027     .0002486    .0041587
         14  |   .0019817   .0009861     2.01   0.044      .000049    .0039145
         15  |   .0017821   .0009675     1.84   0.065    -.0001141    .0036784
         16  |   .0016026    .000943     1.70   0.089    -.0002457    .0034509
------------------------------------------------------------------------------

. 
. * Figure 3.4
. xtcloglog urbancivicny gdppcgrow1yrl c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     10,980
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          9
                                                              avg =       67.0
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      33.01
Log pseudolikelihood  = -306.04863              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 164 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
           urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
          gdppcgrow1yrl |   1.023162   .0099809     2.35   0.019     1.003786    1.042912
                  time1 |   .9220375   .0741837    -1.01   0.313     .7875245    1.079526
                        |
        c.time1#c.time1 |   1.001959   .0013547     1.45   0.148      .999307    1.004617
                        |
c.time1#c.time1#c.time1 |   .9999907   6.81e-06    -1.37   0.171     .9999773    1.000004
                        |
                  _cons |   .0009143   .0015393    -4.16   0.000     .0000337    .0247856
------------------------+----------------------------------------------------------------
               /lnsig2u |  -1.796819   1.469043                      -4.67609    1.082451
------------------------+----------------------------------------------------------------
                sigma_u |   .4072168   .2991094                      .0965162    1.718111
                    rho |   .0915779   .1222117                      .0056312    .6421599
-----------------------------------------------------------------------------------------

. margins, at(gdppcgrow1yrl=(-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10)) predict(pr)

Predictive margins                              Number of obs     =     10,980
Model VCE    : Robust

Expression   : Pr(urbancivicny=1), predict(pr)

1._at        : gdppcgrow1~l    =         -10

2._at        : gdppcgrow1~l    =          -9

3._at        : gdppcgrow1~l    =          -8

4._at        : gdppcgrow1~l    =          -7

5._at        : gdppcgrow1~l    =          -6

6._at        : gdppcgrow1~l    =          -5

7._at        : gdppcgrow1~l    =          -4

8._at        : gdppcgrow1~l    =          -3

9._at        : gdppcgrow1~l    =          -2

10._at       : gdppcgrow1~l    =          -1

11._at       : gdppcgrow1~l    =           0

12._at       : gdppcgrow1~l    =           1

13._at       : gdppcgrow1~l    =           2

14._at       : gdppcgrow1~l    =           3

15._at       : gdppcgrow1~l    =           4

16._at       : gdppcgrow1~l    =           5

17._at       : gdppcgrow1~l    =           6

18._at       : gdppcgrow1~l    =           7

19._at       : gdppcgrow1~l    =           8

20._at       : gdppcgrow1~l    =           9

21._at       : gdppcgrow1~l    =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0035051   .0006805     5.15   0.000     .0021713    .0048389
          2  |    .003586   .0006731     5.33   0.000     .0022666    .0049053
          3  |   .0036688   .0006661     5.51   0.000     .0023631    .0049744
          4  |   .0037534   .0006597     5.69   0.000     .0024605    .0050464
          5  |     .00384   .0006539     5.87   0.000     .0025583    .0051217
          6  |   .0039286   .0006491     6.05   0.000     .0026564    .0052009
          7  |   .0040193   .0006455     6.23   0.000     .0027542    .0052843
          8  |    .004112   .0006432     6.39   0.000     .0028513    .0053727
          9  |   .0042068   .0006426     6.55   0.000     .0029473    .0054664
         10  |   .0043039   .0006441     6.68   0.000     .0030415    .0055662
         11  |   .0044031   .0006477     6.80   0.000     .0031336    .0056727
         12  |   .0045047    .000654     6.89   0.000     .0032229    .0057864
         13  |   .0046085   .0006631     6.95   0.000      .003309    .0059081
         14  |   .0047148   .0006753     6.98   0.000     .0033913    .0060383
         15  |   .0048235   .0006909     6.98   0.000     .0034694    .0061776
         16  |   .0049347   .0007101     6.95   0.000     .0035429    .0063264
         17  |   .0050484   .0007331     6.89   0.000     .0036116    .0064852
         18  |   .0051647     .00076     6.80   0.000     .0036752    .0066543
         19  |   .0052837   .0007909     6.68   0.000     .0037335    .0068339
         20  |   .0054055    .000826     6.54   0.000     .0037865    .0070244
         21  |     .00553   .0008653     6.39   0.000     .0038341    .0072259
------------------------------------------------------------------------------

. xtcloglog leftistny gdppcgrow1yrl c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     10,980
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          9
                                                              avg =       67.0
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      49.72
Log pseudolikelihood  = -368.21201              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 164 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
              leftistny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
          gdppcgrow1yrl |   .9457661   .0112912    -4.67   0.000     .9238926    .9681574
                  time1 |   .9412079   .0323814    -1.76   0.078      .879834    1.006863
                        |
        c.time1#c.time1 |    1.00184   .0007972     2.31   0.021     1.000279    1.003404
                        |
c.time1#c.time1#c.time1 |   .9999845   5.18e-06    -2.99   0.003     .9999743    .9999947
                        |
                  _cons |   .0140165   .0067618    -8.85   0.000     .0054451    .0360805
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.8113529   .6521318                     -2.089508     .466802
------------------------+----------------------------------------------------------------
                sigma_u |   .6665258   .2173313                      .3517784    1.262888
                    rho |   .2126453   .1091847                      .0699662     .492276
-----------------------------------------------------------------------------------------

. margins, at(gdppcgrow1yrl=(-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10)) predict(pr)

Predictive margins                              Number of obs     =     10,980
Model VCE    : Robust

Expression   : Pr(leftistny=1), predict(pr)

1._at        : gdppcgrow1~l    =         -10

2._at        : gdppcgrow1~l    =          -9

3._at        : gdppcgrow1~l    =          -8

4._at        : gdppcgrow1~l    =          -7

5._at        : gdppcgrow1~l    =          -6

6._at        : gdppcgrow1~l    =          -5

7._at        : gdppcgrow1~l    =          -4

8._at        : gdppcgrow1~l    =          -3

9._at        : gdppcgrow1~l    =          -2

10._at       : gdppcgrow1~l    =          -1

11._at       : gdppcgrow1~l    =           0

12._at       : gdppcgrow1~l    =           1

13._at       : gdppcgrow1~l    =           2

14._at       : gdppcgrow1~l    =           3

15._at       : gdppcgrow1~l    =           4

16._at       : gdppcgrow1~l    =           5

17._at       : gdppcgrow1~l    =           6

18._at       : gdppcgrow1~l    =           7

19._at       : gdppcgrow1~l    =           8

20._at       : gdppcgrow1~l    =           9

21._at       : gdppcgrow1~l    =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0106249    .001946     5.46   0.000     .0068108    .0144389
          2  |   .0100558    .001769     5.68   0.000     .0065887     .013523
          3  |   .0095169   .0016099     5.91   0.000     .0063615    .0126722
          4  |   .0090065   .0014677     6.14   0.000     .0061299    .0118831
          5  |   .0085232   .0013413     6.35   0.000     .0058943    .0111521
          6  |   .0080656   .0012298     6.56   0.000     .0056551     .010476
          7  |   .0076323   .0011323     6.74   0.000     .0054131    .0098515
          8  |   .0072221   .0010476     6.89   0.000     .0051687    .0092754
          9  |   .0068337   .0009749     7.01   0.000     .0049228    .0087446
         10  |   .0064661   .0009131     7.08   0.000     .0046763    .0082558
         11  |    .006118   .0008611     7.10   0.000     .0044303    .0078058
         12  |   .0057886   .0008178     7.08   0.000     .0041858    .0073914
         13  |   .0054768    .000782     7.00   0.000     .0039442    .0070094
         14  |   .0051817   .0007526     6.89   0.000     .0037067    .0066567
         15  |   .0049024   .0007285     6.73   0.000     .0034745    .0063303
         16  |    .004638   .0007088     6.54   0.000     .0032488    .0060273
         17  |   .0043879   .0006926     6.34   0.000     .0030305    .0057452
         18  |   .0041511   .0006789     6.11   0.000     .0028204    .0054818
         19  |   .0039271   .0006673     5.89   0.000     .0026193    .0052349
         20  |   .0037151    .000657     5.65   0.000     .0024274    .0050028
         21  |   .0035145   .0006477     5.43   0.000      .002245     .004784
------------------------------------------------------------------------------

. 
. * Economic growth, using the previous three years prior to onset
. xtcloglog leftistny gdppcgrow3yrsl c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     10,849
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          7
                                                              avg =       66.2
                                                              max =        112

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      32.51
Log pseudolikelihood  = -366.20592              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 164 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
              leftistny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
         gdppcgrow3yrsl |   .9685276    .008843    -3.50   0.000     .9513497    .9860157
                  time1 |   .9116347   .0350698    -2.40   0.016     .8454266    .9830279
                        |
        c.time1#c.time1 |   1.002497   .0008716     2.87   0.004      1.00079    1.004207
                        |
c.time1#c.time1#c.time1 |   .9999805   5.70e-06    -3.42   0.001     .9999693    .9999917
                        |
                  _cons |   .0236263   .0126964    -6.97   0.000     .0082409    .0677356
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.8347271   .6445816                     -2.098084    .4286296
------------------------+----------------------------------------------------------------
                sigma_u |   .6587814   .2123192                      .3502732    1.239013
                    rho |   .2087581   .1064708                      .0694102    .4827392
-----------------------------------------------------------------------------------------

. xtcloglog urbancivicny gdppcgrow3yrsl c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     10,849
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          7
                                                              avg =       66.2
                                                              max =        112

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      32.21
Log pseudolikelihood  = -304.82928              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 164 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
           urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
         gdppcgrow3yrsl |   1.015101   .0043938     3.46   0.001     1.006525    1.023749
                  time1 |   .9025563   .0779243    -1.19   0.235     .7620508    1.068968
                        |
        c.time1#c.time1 |   1.002276   .0014412     1.58   0.114     .9994549    1.005104
                        |
c.time1#c.time1#c.time1 |   .9999892   7.19e-06    -1.50   0.134     .9999751    1.000003
                        |
                  _cons |   .0013299    .002373    -3.71   0.000     .0000403    .0439153
------------------------+----------------------------------------------------------------
               /lnsig2u |  -1.859642   1.558983                     -4.915193    1.195909
------------------------+----------------------------------------------------------------
                sigma_u |   .3946243   .3076063                      .0856406    1.818395
                    rho |   .0864839   .1231666                      .0044389    .6677904
-----------------------------------------------------------------------------------------

. 
. * On the relationship of economic growth to urban civic contention among lower-middle income countries
. ttest gdppcgrow3yrsl if gdppcquartersl==2 & indstate==1, by(urbancivicny)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   4,267    7.553086    .2359672    15.41392    7.090467    8.015704
       1 |      24    13.25563    2.638716    12.92701    7.797028    18.71423
---------+--------------------------------------------------------------------
combined |   4,291    7.584981    .2351814    15.40573    7.123903    8.046058
---------+--------------------------------------------------------------------
    diff |           -5.702541    3.152678               -11.88342    .4783384
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.8088
Ho: diff = 0                                     degrees of freedom =     4289

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0353         Pr(|T| > |t|) = 0.0706          Pr(T > t) = 0.9647

. * Absence of relationship outside of lower-middle incomes
. xtcloglog urbancivicny i.gdppcquartersl##c.gdppcgrow1yrl c.time1##c.time1##c.time1 if indstate==1, vce(robust) e
> form nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     10,980
Group variable: cowcode                         Number of groups  =        164

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          9
                                                              avg =       67.0
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(10)     =      72.92
Log pseudolikelihood  = -298.23054              Prob > chi2       =     0.0000

                                                (Std. Err. adjusted for 164 clusters in cowcode)
------------------------------------------------------------------------------------------------
                               |               Robust
                  urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                gdppcquartersl |
                            2  |   1.135674    .319499     0.45   0.651     .6543124    1.971161
                            3  |   1.120237   .5574428     0.23   0.820     .4224181    2.970828
                            4  |   .1173629   .1237091    -2.03   0.042     .0148697    .9263167
                               |
                 gdppcgrow1yrl |   .9775867   .0154451    -1.43   0.151     .9477787    1.008332
                               |
gdppcquartersl#c.gdppcgrow1yrl |
                            2  |   1.051118    .020754     2.52   0.012     1.011218    1.092592
                            3  |   1.089363   .0498515     1.87   0.061     .9959091    1.191586
                            4  |   .9735261   .0193323    -1.35   0.177     .9363634    1.012164
                               |
                         time1 |   .9216375   .0751456    -1.00   0.317     .7855206    1.081341
                               |
               c.time1#c.time1 |   1.001971   .0013681     1.44   0.149     .9992927    1.004656
                               |
       c.time1#c.time1#c.time1 |   .9999907   6.87e-06    -1.35   0.177     .9999773    1.000004
                               |
                         _cons |   .0009348   .0015908    -4.10   0.000     .0000333    .0262592
-------------------------------+----------------------------------------------------------------
                      /lnsig2u |  -3.572014   8.848644                     -20.91504    13.77101
-------------------------------+----------------------------------------------------------------
                       sigma_u |   .1676282   .7416411                      .0000287    977.9951
                           rho |   .0167954   .1461202                      5.02e-10    .9999983
------------------------------------------------------------------------------------------------

. 
. * Lack of direct bivariate relationship between oil production and revolution
. xtcloglog urbancivicny lnoill c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,560
Group variable: cowcode                         Number of groups  =        162

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         22
                                                              avg =       71.4
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      24.91
Log pseudolikelihood  = -306.89631              Prob > chi2       =     0.0001

                                         (Std. Err. adjusted for 162 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
           urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
                 lnoill |   1.010538    .030809     0.34   0.731     .9519217    1.072763
                  time1 |   .9444761   .0792696    -0.68   0.496     .8012164    1.113351
                        |
        c.time1#c.time1 |   1.001622   .0013847     1.17   0.241     .9989115     1.00434
                        |
c.time1#c.time1#c.time1 |   .9999922   6.89e-06    -1.14   0.256     .9999787    1.000006
                        |
                  _cons |   .0005282   .0009415    -4.23   0.000     .0000161    .0173779
------------------------+----------------------------------------------------------------
               /lnsig2u |  -1.737055   1.410395                     -4.501378    1.027269
------------------------+----------------------------------------------------------------
                sigma_u |    .419569    .295879                      .1053266    1.671354
                    rho |   .0966726   .1231656                       .006699    .6293824
-----------------------------------------------------------------------------------------

. xtcloglog leftistny lnoill c.time1##c.time1##c.time1 if indstate==1, vce(robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     11,560
Group variable: cowcode                         Number of groups  =        162

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         22
                                                              avg =       71.4
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(4)      =      30.60
Log pseudolikelihood  =  -371.9194              Prob > chi2       =     0.0000

                                         (Std. Err. adjusted for 162 clusters in cowcode)
-----------------------------------------------------------------------------------------
                        |               Robust
              leftistny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
                 lnoill |   1.057081   .0398787     1.47   0.141     .9817396    1.138204
                  time1 |   .9528851   .0334684    -1.37   0.169     .8894951    1.020793
                        |
        c.time1#c.time1 |   1.001618   .0007878     2.06   0.040     1.000075    1.003163
                        |
c.time1#c.time1#c.time1 |   .9999858   5.01e-06    -2.83   0.005      .999976    .9999956
                        |
                  _cons |   .0080904   .0039277    -9.92   0.000     .0031241    .0209512
------------------------+----------------------------------------------------------------
               /lnsig2u |  -.7691891   .6475971                     -2.038456    .5000778
------------------------+----------------------------------------------------------------
                sigma_u |   .6807266   .2204183                      .3608734    1.284075
                    rho |   .2197902   .1110515                       .073362    .5005944
-----------------------------------------------------------------------------------------

. 
. * ==========================================================
. * MODELS FOR URBAN CIVIC REVOLUTIONARY EPISODES (TABLE 3.1)
. * ==========================================================
. * Table 3.1:  Model for explaining urban civic revolutionary episodes 
. *       Territories with colonial status, territories of independent states, and states under foreign occupation
>  excluded from sample
. 
. * Create common sample for comparison of BIC and AIC
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 yrsincleaderinpower v2x
> _execorr lnoill postcoldwar if  indstate==1, eform nolog

. generate sample=0

. replace sample=1 if e(sample)
(10,494 real changes made)

. *  Likelihood ratio test for whether inclusion of time controls is necessary when postcoldwar dummy is included
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 postcoldwar if indstate
> ==1

. estimates store mod1

. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 postcoldwar time1 times
> q timecub if indstate==1

. estimates store mod2

. lrtest mod1 mod2, stats

Likelihood-ratio test                                 LR chi2(3)  =      1.27
(Assumption: mod1 nested in mod2)                     Prob > chi2 =    0.7365

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
        mod1 |     10,611         .  -273.7069       9    565.4138   630.8406
        mod2 |     10,611         .  -273.0724      12    570.1448   657.3805
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * RESULT:  No need to include time controls if postcoldwar included
. drop _est_mod1 _est_mod2

. 
. * Model 1, with robust errors
. xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 postcoldwar if indstate==1, vce(
> robust) eform nolog

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     10,611
Group variable: cowcode                         Number of groups  =        159

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          9
                                                              avg =       66.7
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(7)      =      94.29
Log pseudolikelihood  =  -273.7069              Prob > chi2       =     0.0000

                               (Std. Err. adjusted for 159 clusters in cowcode)
-------------------------------------------------------------------------------
              |               Robust
 urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       lnpopl |   1.254499   .1157297     2.46   0.014     1.046997    1.503125
     gdppcthl |   1.462175   .1614612     3.44   0.001      1.17762    1.815489
    gdppcthl2 |   .9767168   .0073633    -3.12   0.002     .9623911    .9912557
gdppcgrow1yrl |   1.016118    .010218     1.59   0.112     .9962873    1.036344
      polityl |   .9120294   .0261691    -3.21   0.001     .8621544    .9647895
     polityl2 |   .9829544   .0052482    -3.22   0.001     .9727218    .9932946
  postcoldwar |   8.778389   3.614825     5.28   0.000     3.916528    19.67562
        _cons |   .0001073   .0001172    -8.37   0.000     .0000126    .0009128
--------------+----------------------------------------------------------------
     /lnsig2u |  -2.181044   3.445429                      -8.93396    4.571873
--------------+----------------------------------------------------------------
      sigma_u |   .3360411   .5789028                      .0114819     9.83489
          rho |   .0642393   .2071138                      .0000801    .9832781
-------------------------------------------------------------------------------

. xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 postcoldwar if indstate==1, nolo
> g eform

Random-effects complementary log-log model      Number of obs     =     10,611
Group variable: cowcode                         Number of groups  =        159

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          9
                                                              avg =       66.7
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(7)      =      59.77
Log likelihood  =  -273.7069                    Prob > chi2       =     0.0000

-------------------------------------------------------------------------------
 urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       lnpopl |   1.254499   .1222466     2.33   0.020     1.036391    1.518508
     gdppcthl |   1.462175   .1967225     2.82   0.005     1.123254     1.90336
    gdppcthl2 |   .9767168    .010481    -2.20   0.028      .956389    .9974767
gdppcgrow1yrl |   1.016118   .0186123     0.87   0.383     .9802857     1.05326
      polityl |   .9120294   .0251732    -3.34   0.001     .8640017    .9627268
     polityl2 |   .9829544   .0055716    -3.03   0.002     .9720946    .9939354
  postcoldwar |   8.778389   3.664839     5.20   0.000     3.873036    19.89656
        _cons |   .0001073    .000119    -8.24   0.000     .0000122    .0009427
--------------+----------------------------------------------------------------
     /lnsig2u |  -2.181044   3.820699                     -9.669476    5.307389
--------------+----------------------------------------------------------------
      sigma_u |   .3360411    .641956                      .0079488    14.20643
          rho |   .0642393   .2296723                      .0000384    .9919155
-------------------------------------------------------------------------------
LR test of rho=0: chibar2(01) = 0.07                   Prob >= chibar2 = 0.395

. quadchk , nooutput

Refitting model intpoints() =  8
Refitting model intpoints() = 16

                         Quadrature check

               Fitted       Comparison     Comparison
             quadrature     quadrature     quadrature
             12 points      8 points       16 points
-----------------------------------------------------
Log           -273.7069      -273.7069      -273.7069
likelihood                   5.359e-09     -7.162e-12   Difference
                            -1.958e-11      2.617e-14   Relative difference
-----------------------------------------------------
urbancivicny: .22673636      .22673636      .22673636
  lnpopl                    -4.317e-12      3.017e-10   Difference
                            -1.904e-11      1.331e-09   Relative difference
-----------------------------------------------------
urbancivicny: .37992507      .37992507      .37992507
gdppcthl                    -3.034e-12      2.019e-10   Difference
                            -7.985e-12      5.315e-10   Relative difference
-----------------------------------------------------
urbancivicny:-.02355852     -.02355852     -.02355852
gdppcthl2                    1.901e-13     -1.299e-11   Difference
                            -8.068e-12      5.512e-10   Relative difference
-----------------------------------------------------
urbancivicny: .01598961      .01598961      .01598961
gdppcgrow1~l                 1.105e-13     -7.142e-12   Difference
                             6.910e-12     -4.467e-10   Relative difference
-----------------------------------------------------
urbancivicny: -.0920831      -.0920831      -.0920831
 polityl                     1.858e-12     -1.272e-10   Difference
                            -2.017e-11      1.382e-09   Relative difference
-----------------------------------------------------
urbancivicny: -.0171926      -.0171926      -.0171926
polityl2                     5.217e-14     -3.374e-12   Difference
                            -3.034e-12      1.963e-10   Relative difference
-----------------------------------------------------
urbancivicny: 2.1722929      2.1722929      2.1722929
postcoldwar                 -7.070e-12      4.762e-10   Difference
                            -3.255e-12      2.192e-10   Relative difference
-----------------------------------------------------
urbancivicny:-9.1398888     -9.1398888     -9.1398888
   _cons                     9.075e-11     -6.122e-09   Difference
                            -9.929e-12      6.698e-10   Relative difference
-----------------------------------------------------
lnsig2u:     -2.1810436     -2.1810436     -2.1810436
   _cons                    -7.655e-10      3.859e-08   Difference
                             3.510e-10     -1.769e-08   Relative difference
-----------------------------------------------------

. * Passed
. * Compute AIC/BIC
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 postcoldwar if indstate
> ==1 & sample==1, nolog eform

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |     10,494         .  -273.4041       9    564.8082   630.1353
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Computing percent correctly predicted with above-average risk
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 postcoldwar if indstate
> ==1, vce(robust) eform nolog

. predict prmod1, pr
(using 12 quadrature points)
(7644 missing values generated)

. * Correctly predicted positive cases
. sum urbancivicny if e(sample)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
urbancivicny |     10,611    .0047121     .068486          0          1

. local prmean=r(mean)

. count if urbancivicny==1 & prmod1>`prmean' & e(sample)
  39

. local corposcount=r(N)

. count if urbancivicny==1 & prmod1<=`prmean' & e(sample)
  11

. local falseposcount=r(N)

. display (`corposcount')/(`corposcount' + `falseposcount')
.78

. * Correctly predicted negative cases
. count if urbancivicny==0 & prmod1>`prmean' & e(sample)
  2,707

. local falsenegcount=r(N)

. count if urbancivicny==0 & prmod1<=`prmean' & e(sample)
  7,854

. local cornegcount=r(N)

. display (`falsenegcount')/(`cornegcount' + `falsenegcount')
.25632042

. * AUC
. roctab urbancivicny prmod1 if e(sample)

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
        10,611     0.8412       0.0265        0.78932     0.89302

. macro drop _all

. drop prmod1

. 
. * Model 2, with robust errors
. xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gedpartyautoc gedmilautoc gedmonautoc gedpersautoc postcoldwar 
> if indstate==1,  eform nolog vce(robust)

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =      7,690
Group variable: cowcode                         Number of groups  =        148

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         17
                                                              avg =       52.0
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(8)      =     145.52
Log pseudolikelihood  = -215.69631              Prob > chi2       =     0.0000

                               (Std. Err. adjusted for 148 clusters in cowcode)
-------------------------------------------------------------------------------
              |               Robust
 urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       lnpopl |   1.244136   .1380507     1.97   0.049     1.000962    1.546386
     gdppcthl |   1.436001   .1776377     2.93   0.003      1.12683        1.83
    gdppcthl2 |   .9773042   .0072597    -3.09   0.002     .9631785    .9916371
gedpartyautoc |   3.312838   1.865038     2.13   0.033     1.098998    9.986275
  gedmilautoc |   15.26314   9.186669     4.53   0.000     4.691564    49.65581
  gedmonautoc |   2.727148   3.452572     0.79   0.428     .2280797     32.6085
 gedpersautoc |   7.232602   3.926891     3.64   0.000     2.495403    20.96276
  postcoldwar |   7.823159   3.076695     5.23   0.000     3.619273    16.90998
        _cons |   .0000218   .0000264    -8.86   0.000     2.03e-06    .0002339
--------------+----------------------------------------------------------------
     /lnsig2u |  -2.828529   8.868169                     -20.20982    14.55276
--------------+----------------------------------------------------------------
      sigma_u |   .2431044   1.077945                      .0000409    1445.747
          rho |   .0346823   .2969009                      1.02e-09    .9999992
-------------------------------------------------------------------------------

. xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gedpartyautoc gedmilautoc gedmonautoc gedpersautoc postcoldwar 
> if indstate==1,  eform nolog

Random-effects complementary log-log model      Number of obs     =      7,690
Group variable: cowcode                         Number of groups  =        148

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         17
                                                              avg =       52.0
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(8)      =      50.98
Log likelihood  = -215.69631                    Prob > chi2       =     0.0000

-------------------------------------------------------------------------------
 urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       lnpopl |   1.244136    .147074     1.85   0.065     .9868337    1.568525
     gdppcthl |   1.436001   .2000003     2.60   0.009     1.092957    1.886716
    gdppcthl2 |   .9773042   .0105771    -2.12   0.034     .9567918    .9982563
gedpartyautoc |   3.312838   1.795109     2.21   0.027     1.145419    9.581556
  gedmilautoc |   15.26314   8.832006     4.71   0.000     4.910172    47.44507
  gedmonautoc |   2.727148   2.313353     1.18   0.237     .5172036     14.3799
 gedpersautoc |   7.232602   3.967846     3.61   0.000     2.467861    21.19671
  postcoldwar |   7.823159   3.571284     4.51   0.000     3.197476    19.14066
        _cons |   .0000218   .0000298    -7.84   0.000     1.49e-06    .0003187
--------------+----------------------------------------------------------------
     /lnsig2u |  -2.828529    8.36249                     -19.21871    13.56165
--------------+----------------------------------------------------------------
      sigma_u |   .2431044   1.016479                      .0000671    880.7952
          rho |   .0346823   .2799711                      2.74e-09    .9999979
-------------------------------------------------------------------------------
LR test of rho=0: chibar2(01) = 0.01                   Prob >= chibar2 = 0.451

. quadchk , nooutput

Refitting model intpoints() =  8
Refitting model intpoints() = 16

                         Quadrature check

               Fitted       Comparison     Comparison
             quadrature     quadrature     quadrature
             12 points      8 points       16 points
-----------------------------------------------------
Log          -215.69631     -215.69631     -215.69631
likelihood                   3.902e-11      5.306e-11   Difference
                            -1.809e-13     -2.460e-13   Relative difference
-----------------------------------------------------
urbancivicny: .21844108      .21844107      .21844107
  lnpopl                    -1.587e-08     -1.541e-08   Difference
                            -7.267e-08     -7.053e-08   Relative difference
-----------------------------------------------------
urbancivicny: .36186223      .36186222      .36186222
gdppcthl                    -1.219e-08     -1.197e-08   Difference
                            -3.370e-08     -3.309e-08   Relative difference
-----------------------------------------------------
urbancivicny:-.02295732     -.02295732     -.02295732
gdppcthl2                    9.895e-10      9.743e-10   Difference
                            -4.310e-08     -4.244e-08   Relative difference
-----------------------------------------------------
urbancivicny: 1.1978053      1.1978052      1.1978052
gedpartyau~c                -9.709e-08     -9.285e-08   Difference
                            -8.106e-08     -7.752e-08   Relative difference
-----------------------------------------------------
urbancivicny: 2.7254408      2.7254406      2.7254406
gedmilautoc                 -1.964e-07     -1.906e-07   Difference
                            -7.208e-08     -6.994e-08   Relative difference
-----------------------------------------------------
urbancivicny: 1.0032563      1.0032561      1.0032562
gedmonautoc                 -1.834e-07     -1.770e-07   Difference
                            -1.828e-07     -1.764e-07   Relative difference
-----------------------------------------------------
urbancivicny: 1.9785989      1.9785988      1.9785988
gedpersautoc                -1.128e-07     -1.090e-07   Difference
                            -5.700e-08     -5.509e-08   Relative difference
-----------------------------------------------------
urbancivicny: 2.0570884      2.0570883      2.0570883
postcoldwar                 -8.232e-08     -7.963e-08   Difference
                            -4.002e-08     -3.871e-08   Relative difference
-----------------------------------------------------
urbancivicny:-10.735143     -10.735143     -10.735143
   _cons                     5.921e-07      5.727e-07   Difference
                            -5.515e-08     -5.335e-08   Relative difference
-----------------------------------------------------
lnsig2u:     -2.8285288     -2.8285882     -2.8285879
   _cons                     -.0000594     -.00005906   Difference
                               .000021      .00002088   Relative difference
-----------------------------------------------------

. * Passed
. * Computing percent correctly predicted with above-average risk
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gedpartyautoc gedmilautoc gedmonautoc gedpersautoc pos
> tcoldwar if indstate==1,  eform nolog vce(robust)

. predict prmod2, pr
(using 12 quadrature points)
(10691 missing values generated)

. * Correctly predicted positive cases
. sum urbancivicny if e(sample)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
urbancivicny |      7,690    .0052016    .0719387          0          1

. local prmean=r(mean)

. count if urbancivicny==1 & prmod2>`prmean' & e(sample)
  31

. local corposcount=r(N)

. count if urbancivicny==1 & prmod2<=`prmean' & e(sample)
  9

. local falseposcount=r(N)

. display (`corposcount')/(`corposcount' + `falseposcount')
.775

. * Correctly predicted negative cases
. count if urbancivicny==0 & prmod2>`prmean' & e(sample)
  2,129

. local falsenegcount=r(N)

. count if urbancivicny==0 & prmod2<=`prmean' & e(sample)
  5,521

. local cornegcount=r(N)

. display (`falsenegcount')/(`cornegcount' + `falsenegcount')
.27830065

. * AUC
. roctab urbancivicny prmod2 if e(sample)

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
         7,690     0.8450       0.0233        0.79938     0.89062

. macro drop _all

. drop prmod2

. 
. * Model 3, with robust errors
. * 22 integration points used after failing quadrature check at 12 and testing for alternative methods of integra
> tion
. xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 yrsincleaderinpower v2x_execorr 
> lnoill postcoldwar if indstate==1, vce(robust) eform nolog intpoints(22)

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     10,494
Group variable: cowcode                         Number of groups  =        157

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         21
                                                              avg =       66.8
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         22

                                                Wald chi2(10)     =     106.62
Log pseudolikelihood  = -263.48092              Prob > chi2       =     0.0000

                                     (Std. Err. adjusted for 157 clusters in cowcode)
-------------------------------------------------------------------------------------
                    |               Robust
       urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |    1.50806   .1464198     4.23   0.000     1.246735    1.824161
           gdppcthl |   1.805859   .2294703     4.65   0.000     1.407738    2.316573
          gdppcthl2 |   .9678288   .0086085    -3.68   0.000     .9511027    .9848491
      gdppcgrow1yrl |   1.011887   .0146883     0.81   0.416     .9835036    1.041089
            polityl |   .9271132   .0309891    -2.26   0.024     .8683225    .9898845
           polityl2 |   .9842485   .0054542    -2.87   0.004     .9736163    .9949968
yrsincleaderinpower |   1.032988   .0127816     2.62   0.009     1.008238    1.058346
        v2x_execorr |    5.07339   4.377094     1.88   0.060     .9352322    27.52181
             lnoill |   .8794048   .0255707    -4.42   0.000     .8306886     .930978
        postcoldwar |   6.911787   3.052429     4.38   0.000     2.908554    16.42493
              _cons |   6.13e-06   8.25e-06    -8.92   0.000     4.39e-07    .0000856
--------------------+----------------------------------------------------------------
           /lnsig2u |  -11.00724      26607                     -52159.77    52137.76
--------------------+----------------------------------------------------------------
            sigma_u |    .004072    54.1718                             0           .
                rho |   .0000101   .2681966                             0           .
-------------------------------------------------------------------------------------

. xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 yrsincleaderinpower v2x_execorr 
> lnoill postcoldwar if indstate==1, eform nolog intpoints(22)

Random-effects complementary log-log model      Number of obs     =     10,494
Group variable: cowcode                         Number of groups  =        157

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         21
                                                              avg =       66.8
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         22

                                                Wald chi2(10)     =      83.36
Log likelihood  = -263.48092                    Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------
       urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |    1.50806   .1639795     3.78   0.000     1.218605     1.86627
           gdppcthl |   1.805859   .2720449     3.92   0.000      1.34417    2.426128
          gdppcthl2 |   .9678288   .0114867    -2.76   0.006     .9455752    .9906062
      gdppcgrow1yrl |   1.011887   .0227601     0.53   0.599     .9682465    1.057494
            polityl |   .9271132   .0274312    -2.56   0.011     .8748783    .9824669
           polityl2 |   .9842485   .0055675    -2.81   0.005     .9733967    .9952213
yrsincleaderinpower |   1.032988   .0142365     2.35   0.019     1.005459    1.061272
        v2x_execorr |    5.07339   3.401425     2.42   0.015     1.363374     18.8791
             lnoill |   .8794048   .0319663    -3.54   0.000     .8189318    .9443434
        postcoldwar |   6.911787    2.97767     4.49   0.000     2.970871     16.0804
              _cons |   6.13e-06   7.82e-06    -9.42   0.000     5.04e-07    .0000746
--------------------+----------------------------------------------------------------
           /lnsig2u |  -11.00724   46.23797                      -101.632    79.61751
--------------------+----------------------------------------------------------------
            sigma_u |    .004072   .0941404                      8.53e-23    1.94e+17
                rho |   .0000101   .0004661                      4.42e-45           1
-------------------------------------------------------------------------------------
LR test of rho=0: chibar2(01) = 3.6e-05                Prob >= chibar2 = 0.498

. quadchk 12 22, nofrom nooutput

Refitting model intpoints() = 12
Refitting model intpoints() = 22

                         Quadrature check

               Fitted       Comparison     Comparison
             quadrature     quadrature     quadrature
             22 points      12 points      22 points
-----------------------------------------------------
Log          -263.48092     -263.48094     -263.48092
likelihood                  -.00001097     -9.720e-12   Difference
                             4.165e-08      3.689e-14   Relative difference
-----------------------------------------------------
urbancivicny:  .4108241      .41082282       .4108241
  lnpopl                    -1.273e-06     -1.388e-15   Difference
                            -3.099e-06     -3.378e-15   Relative difference
-----------------------------------------------------
urbancivicny: .59103649      .59103731      .59103649
gdppcthl                     8.249e-07     -1.619e-12   Difference
                             1.396e-06     -2.739e-12   Relative difference
-----------------------------------------------------
urbancivicny:-.03270003     -.03270022     -.03270003
gdppcthl2                   -1.921e-07      1.725e-13   Difference
                             5.875e-06     -5.276e-12   Relative difference
-----------------------------------------------------
urbancivicny: .01181647      .01181596      .01181647
gdppcgrow1~l                -5.139e-07     -2.533e-14   Difference
                            -.00004349     -2.144e-12   Relative difference
-----------------------------------------------------
urbancivicny:-.07567957     -.07568143     -.07567957
 polityl                    -1.858e-06      1.524e-14   Difference
                             .00002456     -2.013e-13   Relative difference
-----------------------------------------------------
urbancivicny:-.01587686     -.01587686     -.01587686
polityl2                    -6.371e-09      8.566e-15   Difference
                             4.013e-07     -5.395e-13   Relative difference
-----------------------------------------------------
urbancivicny: .03245595       .0324576      .03245595
yrsinclead~r                 1.649e-06      1.367e-13   Difference
                              .0000508      4.212e-12   Relative difference
-----------------------------------------------------
urbancivicny: 1.6240092      1.6239624      1.6240092
v2x_execorr                 -.00004673     -3.090e-12   Difference
                            -.00002877     -1.903e-12   Relative difference
-----------------------------------------------------
urbancivicny:-.12850999     -.12851003     -.12850999
  lnoill                    -4.060e-08      4.277e-14   Difference
                             3.159e-07     -3.328e-13   Relative difference
-----------------------------------------------------
urbancivicny: 1.9332282       1.933247      1.9332282
postcoldwar                  .00001878      3.957e-13   Difference
                             9.713e-06      2.047e-13   Relative difference
-----------------------------------------------------
urbancivicny:-12.001998     -12.002045     -12.001998
   _cons                     -.0000466     -1.428e-12   Difference
                             3.883e-06      1.190e-13   Relative difference
-----------------------------------------------------
lnsig2u:     -11.007244     -10.564013     -11.007244
   _cons                     .44323062      4.765e-07   Difference
                            -.04026717     -4.329e-08   Relative difference
-----------------------------------------------------

. * Passed
. * Compute AIC/BIC
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 yrsincleaderinpower v2x
> _execorr lnoill postcoldwar if indstate==1 & sample==1, eform nolog intpoints(22)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |     10,494         .  -263.4809      12    550.9618   638.0646
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Computing percent correctly predicted with above-average risk
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 gdppcgrow1yrl polityl polityl2 yrsincleaderinpower v2x
> _execorr lnoill postcoldwar if indstate==1, vce(robust) eform nolog intpoints(22)

. predict prmod3, pr
(using 22 quadrature points)
(7809 missing values generated)

. * Correctly predicted positive cases
. sum urbancivicny if e(sample)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
urbancivicny |     10,494    .0047646    .0688649          0          1

. local prmean=r(mean)

. count if urbancivicny==1 & prmod3>`prmean' & e(sample)
  38

. local corposcount=r(N)

. count if urbancivicny==1 & prmod3<=`prmean' & e(sample)
  12

. local falseposcount=r(N)

. display (`corposcount')/(`corposcount' + `falseposcount')
.76

. * Correctly predicted negative cases
. count if urbancivicny==0 & prmod3>`prmean' & e(sample)
  2,475

. local falsenegcount=r(N)

. count if urbancivicny==0 & prmod3<=`prmean' & e(sample)
  7,969

. local cornegcount=r(N)

. display (`falsenegcount')/(`cornegcount' + `falsenegcount')
.23697817

. * AUC
. roctab urbancivicny prmod3 if e(sample)

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
        10,494     0.8564       0.0275        0.80254     0.91031

. macro drop _all

. drop prmod3

. 
. * Model 4, with robust errors
. * 22 integration points used after failing quadrature check at 12 and testing for alternative methods of integra
> tion
. xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoill postcol
> dwar if indstate==1, vce(robust) eform nolog intpoints(22)

Calculating robust standard errors:

Random-effects complementary log-log model      Number of obs     =     10,516
Group variable: cowcode                         Number of groups  =        157

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         21
                                                              avg =       67.0
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         22

                                                Wald chi2(9)      =      96.48
Log pseudolikelihood  = -263.63108              Prob > chi2       =     0.0000

                                     (Std. Err. adjusted for 157 clusters in cowcode)
-------------------------------------------------------------------------------------
                    |               Robust
       urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.518911   .1462595     4.34   0.000     1.257674    1.834411
           gdppcthl |   1.824336   .2345283     4.68   0.000     1.418007    2.347099
          gdppcthl2 |   .9673956   .0086959    -3.69   0.000     .9505011    .9845904
            polityl |   .9269702   .0307108    -2.29   0.022     .8686908    .9891595
           polityl2 |   .9841294   .0054603    -2.88   0.004     .9734854    .9948897
yrsincleaderinpower |   1.033031   .0125852     2.67   0.008     1.008656    1.057994
        v2x_execorr |   5.163815   4.423932     1.92   0.055     .9632323    27.68281
             lnoill |   .8778001   .0259313    -4.41   0.000     .8284191    .9301246
        postcoldwar |   6.900153   3.029986     4.40   0.000     2.917967    16.31688
              _cons |   5.73e-06   7.66e-06    -9.03   0.000     4.17e-07    .0000788
--------------------+----------------------------------------------------------------
           /lnsig2u |  -10.91173   21934.54                     -43001.83       42980
--------------------+----------------------------------------------------------------
            sigma_u |   .0042712   46.84308                             0           .
                rho |   .0000111   .2432564                             0           .
-------------------------------------------------------------------------------------

. xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoill postcol
> dwar if indstate==1, eform nolog intpoints(22)

Random-effects complementary log-log model      Number of obs     =     10,516
Group variable: cowcode                         Number of groups  =        157

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         21
                                                              avg =       67.0
                                                              max =        114

Integration method: mvaghermite                 Integration pts.  =         22

                                                Wald chi2(9)      =      82.87
Log likelihood  = -263.63108                    Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------
       urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.518911   .1653275     3.84   0.000     1.227107    1.880106
           gdppcthl |   1.824336   .2727781     4.02   0.000     1.360917    2.445559
          gdppcthl2 |   .9673956   .0114603    -2.80   0.005     .9451926    .9901202
            polityl |   .9269702   .0274557    -2.56   0.010     .8746903    .9823749
           polityl2 |   .9841294   .0055478    -2.84   0.005     .9733158    .9950631
yrsincleaderinpower |   1.033031   .0141881     2.37   0.018     1.005593    1.061216
        v2x_execorr |   5.163815   3.462612     2.45   0.014     1.387378     19.2197
             lnoill |   .8778001   .0318743    -3.59   0.000     .8174989    .9425493
        postcoldwar |   6.900153   2.971243     4.49   0.000     2.967063    16.04688
              _cons |   5.73e-06   7.31e-06    -9.46   0.000     4.70e-07    .0000698
--------------------+----------------------------------------------------------------
           /lnsig2u |  -10.91173   47.89722                     -104.7886    82.96509
--------------------+----------------------------------------------------------------
            sigma_u |   .0042712   .1022886                      1.76e-23    1.04e+18
                rho |   .0000111   .0005312                      1.88e-46           1
-------------------------------------------------------------------------------------
LR test of rho=0: chibar2(01) = 3.5e-05                Prob >= chibar2 = 0.498

. quadchk 12 22, nofrom nooutput

Refitting model intpoints() = 12
Refitting model intpoints() = 22

                         Quadrature check

               Fitted       Comparison     Comparison
             quadrature     quadrature     quadrature
             22 points      12 points      22 points
-----------------------------------------------------
Log          -263.63108     -263.63109     -263.63108
likelihood                  -.00001053     -1.262e-11   Difference
                             3.993e-08      4.787e-14   Relative difference
-----------------------------------------------------
urbancivicny: .41799385      .41799214      .41799385
  lnpopl                    -1.710e-06     -5.423e-14   Difference
                            -4.090e-06     -1.297e-13   Relative difference
-----------------------------------------------------
urbancivicny: .60121634      .60121723      .60121634
gdppcthl                     8.918e-07     -1.794e-12   Difference
                             1.483e-06     -2.983e-12   Relative difference
-----------------------------------------------------
urbancivicny:-.03314778     -.03314801     -.03314778
gdppcthl2                   -2.286e-07      1.850e-13   Difference
                             6.897e-06     -5.582e-12   Relative difference
-----------------------------------------------------
urbancivicny:-.07583382     -.07583585     -.07583382
 polityl                    -2.024e-06     -4.750e-14   Difference
                             .00002669      6.264e-13   Relative difference
-----------------------------------------------------
urbancivicny:-.01599791      -.0159979     -.01599791
polityl2                     1.510e-08      1.273e-14   Difference
                            -9.439e-07     -7.957e-13   Relative difference
-----------------------------------------------------
urbancivicny:  .0324968      .03249862       .0324968
yrsinclead~r                 1.827e-06      2.242e-13   Difference
                             .00005622      6.899e-12   Relative difference
-----------------------------------------------------
urbancivicny: 1.6416756      1.6416256      1.6416756
v2x_execorr                 -.00004999     -5.199e-12   Difference
                            -.00003045     -3.167e-12   Relative difference
-----------------------------------------------------
urbancivicny:-.13033639     -.13033641     -.13033639
  lnoill                    -1.585e-08      5.335e-14   Difference
                             1.216e-07     -4.093e-13   Relative difference
-----------------------------------------------------
urbancivicny: 1.9315436      1.9315639      1.9315436
postcoldwar                   .0000203      1.028e-12   Difference
                             .00001051      5.324e-13   Relative difference
-----------------------------------------------------
urbancivicny:-12.070022     -12.070072     -12.070022
   _cons                    -.00005001     -3.480e-12   Difference
                             4.143e-06      2.883e-13   Relative difference
-----------------------------------------------------
lnsig2u:     -10.911735     -10.470038     -10.911734
   _cons                     .44169682      6.478e-07   Difference
                            -.04047906     -5.936e-08   Relative difference
-----------------------------------------------------

. * Passed
. * Compute AIC/BIC
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoil
> l postcoldwar if indstate==1 & sample==1, eform nolog intpoints(22)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |     10,494         .  -263.6152      11    549.2305   629.0746
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Computing percent correctly predicted with above-average risk
. quietly: xtcloglog urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoil
> l postcoldwar if indstate==1, vce(robust) eform nolog intpoints(22)

. predict prmod4, pr
(using 22 quadrature points)
(7786 missing values generated)

. * Correctly predicted positive cases
. sum urbancivicny if e(sample)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
urbancivicny |     10,516    .0047547    .0687932          0          1

. local prmean=r(mean)

. count if urbancivicny==1 & prmod4>`prmean' & e(sample)
  38

. local corposcount=r(N)

. count if urbancivicny==1 & prmod4<=`prmean' & e(sample)
  12

. local falseposcount=r(N)

. display (`corposcount')/(`corposcount' + `falseposcount')
.76

. * Correctly predicted negative cases
. count if urbancivicny==0 & prmod4>`prmean' & e(sample)
  2,476

. local falsenegcount=r(N)

. count if urbancivicny==0 & prmod4<=`prmean' & e(sample)
  7,990

. local cornegcount=r(N)

. display (`falsenegcount')/(`cornegcount' + `falsenegcount')
.23657558

. * AUC
. roctab urbancivicny prmod4 if e(sample)

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
        10,516     0.8566       0.0275        0.80277     0.91041

. macro drop _all

. drop prmod4

. 
. * ++++++++++++++
. * Pooled models
. * ++++++++++++++
. * Pooled complementary log-log model
. cloglog urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoill postcoldw
> ar if indstate==1 , vce(robust) eform nolog

Complementary log-log regression                Number of obs     =     10,516
                                                Zero outcomes     =     10,466
                                                Nonzero outcomes  =         50

                                                Wald chi2(9)      =      74.72
Log pseudolikelihood = -263.63106               Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------
                    |               Robust
       urbancivicny |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.518912   .1639994     3.87   0.000     1.229212    1.876887
           gdppcthl |   1.824335   .2536675     4.32   0.000     1.389146    2.395859
          gdppcthl2 |   .9673957   .0091696    -3.50   0.000     .9495895    .9855357
            polityl |   .9269707   .0306614    -2.29   0.022     .8687821    .9890567
           polityl2 |   .9841294   .0051772    -3.04   0.002     .9740343     .994329
yrsincleaderinpower |    1.03303   .0120824     2.78   0.005     1.009618    1.056985
        v2x_execorr |   5.163875   4.032023     2.10   0.036     1.117754    23.85642
             lnoill |   .8778001   .0297584    -3.84   0.000     .8213702    .9381068
        postcoldwar |   6.900119   2.907724     4.58   0.000     3.021055    15.75994
              _cons |   5.73e-06   8.05e-06    -8.59   0.000     3.65e-07      .00009
-------------------------------------------------------------------------------------

. * Compute AIC/BIC
. quietly: cloglog urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoill 
> postcoldwar if indstate==1 & sample==1, eform nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |     10,494 -317.2075  -263.6152      10    547.2304    619.816
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Computing percent correctly predicted with above-average risk
. quietly: cloglog urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoill 
> postcoldwar if indstate==1 , vce(robust) eform

. predict prmod5, pr
(7,786 missing values generated)

. * Correctly predicted positive cases
. sum urbancivicny if e(sample)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
urbancivicny |     10,516    .0047547    .0687932          0          1

. local prmean=r(mean)

. count if urbancivicny==1 & prmod5>`prmean' & e(sample)
  38

. local corposcount=r(N)

. count if urbancivicny==1 & prmod5<=`prmean' & e(sample)
  12

. local falseposcount=r(N)

. display (`corposcount')/(`corposcount' + `falseposcount')
.76

. * Incorrectly predicted negative cases
. count if urbancivicny==0 & prmod5>`prmean' & e(sample)
  2,476

. local falsenegcount=r(N)

. count if urbancivicny==0 & prmod5<=`prmean' & e(sample)
  7,990

. local cornegcount=r(N)

. display (`falsenegcount')/(`cornegcount' + `falsenegcount')
.23657558

. macro drop _all

. drop prmod5

. * Area under the ROC curve
. quietly:  cloglog urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoill
>  postcoldwar if indstate==1 , vce(robust) eform

. predict double xb, xb
(7,786 missing values generated)

. roctab urbancivicny xb if e(sample)

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
        10,516     0.8566       0.0275        0.80277     0.91041

. drop xb

. 
. * Firth penalized logit
. firthlogit urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoill postco
> ldwar if indstate==1, or nolog

                                                Number of obs     =     10,516
                                                Wald chi2(9)      =      81.39
Penalized log likelihood = -233.87782           Prob > chi2       =     0.0000

-------------------------------------------------------------------------------------
       urbancivicny | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.524346   .1680122     3.82   0.000     1.228188    1.891918
           gdppcthl |   1.744549   .2641513     3.68   0.000     1.296577    2.347297
          gdppcthl2 |   .9718688   .0114051    -2.43   0.015     .9497703    .9944814
            polityl |   .9287739   .0271832    -2.52   0.012     .8769952    .9836097
           polityl2 |   .9846501   .0055482    -2.75   0.006     .9738358    .9955846
yrsincleaderinpower |   1.034714   .0143301     2.46   0.014     1.007005    1.063185
        v2x_execorr |   4.842704   3.237719     2.36   0.018     1.306154    17.95484
             lnoill |   .8787834   .0322708    -3.52   0.000     .8177563    .9443648
        postcoldwar |   6.523729   2.742933     4.46   0.000     2.861568    14.87263
              _cons |   6.85e-06   8.78e-06    -9.29   0.000     5.57e-07    .0000843
-------------------------------------------------------------------------------------

. * Compute AIC/BIC
. quietly: firthlogit urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoi
> ll postcoldwar if indstate==1 & sample==1,  or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |     10,494         .  -233.8621      10    487.7243   560.3099
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Computing percent correctly predicted with above-average risk
. quietly: firthlogit urbancivicny lnpopl gdppcthl gdppcthl2 polityl polityl2 yrsincleaderinpower v2x_execorr lnoi
> ll postcoldwar if indstate==1, or nolog

. predict prmod6, xb
(7,786 missing values generated)

. quietly replace prmod6 = invlogit(prmod6)

. * Correctly predicted positive cases
. sum urbancivicny if e(sample)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
urbancivicny |     10,516    .0047547    .0687932          0          1

. local prmean=r(mean)

. count if urbancivicny==1 & prmod6>`prmean' & e(sample)
  40

. local corposcount=r(N)

. count if urbancivicny==1 & prmod6<=`prmean' & e(sample)
  10

. local falseposcount=r(N)

. display (`corposcount')/(`corposcount' + `falseposcount')
.8

. * Correctly predicted negative cases
. count if urbancivicny==0 & prmod6>`prmean' & e(sample)
  2,681

. local falsenegcount=r(N)

. count if urbancivicny==0 & prmod6<=`prmean' & e(sample)
  7,785

. local cornegcount=r(N)

. display (`falsenegcount')/(`cornegcount' + `falsenegcount')
.25616281

. macro drop _all

. drop prmod6

. * Area under the ROC curve
. predict double xb, xb
(7,786 missing values generated)

. roctab urbancivicny xb if e(sample)

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
        10,516     0.8556       0.0279        0.80080     0.91033

. drop xb

. *
. * Drop comparison sample variable
. drop sample

. 
. * ++++++++++++++++++++++++++
. * Multiple imputation models
. * ++++++++++++++++++++++++++
. * Use multiple imputation sample previously generated for 20 imputed samples
. * See robustness tests for details on imputation process and post-imputation tests
. clear

. use revspredictbycntryyrmi.dta

. * Calculate estimation for Model 4--panel complementary log-log model
. mi estimate, post dots eform saving(miest, replace): xtcloglog urbancivicny lnpopl c.gdppcthl##c.gdppcthl c.poli
> tyl##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =     11,742

Group variable: cowcode                         Number of groups  =        164
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
Integration points = 12                                       avg =       71.6
                                                              max =        115
                                                Average RVI       =     0.0039
                                                Largest FMI       =     0.0235
DF adjustment:   Large sample                   DF:     min       =  34,657.90
                                                        avg       =   2.72e+18
                                                        max       =   2.99e+19
Model F test:       Equal FMI                   F(   9, 8.1e+06)  =      11.67
Within VCE type:       Robust                   Prob > F          =     0.0000

                                      (Within VCE adjusted for 164 clusters in cowcode)
---------------------------------------------------------------------------------------
         urbancivicny |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               lnpopl |   1.531639   .1378796     4.74   0.000     1.283898    1.827185
             gdppcthl |    1.83485   .2367177     4.70   0.000     1.424903    2.362739
                      |
c.gdppcthl#c.gdppcthl |   .9668665   .0088675    -3.67   0.000     .9496418    .9844036
                      |
              polityl |    .926198   .0301023    -2.36   0.018     .8690384    .9871171
                      |
  c.polityl#c.polityl |   .9852065   .0054904    -2.67   0.007     .9745036     .996027
                      |
  yrsincleaderinpower |   1.034881   .0121307     2.93   0.003     1.011377    1.058932
          v2x_execorr |   5.257668    4.39035     1.99   0.047     1.023312    27.01335
               lnoill |   .8769103   .0257931    -4.47   0.000     .8277863    .9289495
          postcoldwar |   7.640183   3.286123     4.73   0.000     3.288464    17.75066
                _cons |   4.32e-06   5.49e-06    -9.71   0.000     3.57e-07    .0000522
----------------------+----------------------------------------------------------------
             /lnsig2u |  -10.23687   11385.54                     -22325.49    22305.02
----------------------+----------------------------------------------------------------
              sigma_u |   .0059854   34.07334                             0           .
                  rho |   .0000218   .2479524                             0           .
---------------------------------------------------------------------------------------

. * Model predictions
. mi predict xburbmi using miest, xb

. generate prurbmi = invlogit(xburbmi)

. * Obtain accuracy of prediction
. * True positive rate
. sum urbancivicny if e(N_mi)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
urbancivicny |     11,742    .0043434    .0657639          0          1

. local prmean=r(mean)

. count if urbancivicny==1 & prurbmi>`prmean' 
  39

. local corposcount=r(N)

. count if urbancivicny==1 & prurbmi<=`prmean' 
  12

. local falseposcount=r(N)

. display (`corposcount')/(`corposcount' + `falseposcount')
.76470588

. * False positive rate
. count if urbancivicny==0 & prurbmi>`prmean' 
  2,686

. local falsenegcount=r(N)

. count if urbancivicny==0 & prurbmi<=`prmean'
  9,005

. local cornegcount=r(N)

. display (`falsenegcount')/(`cornegcount' + `falsenegcount')
.22974938

. macro drop _all

. * Area under the ROC curve
. roctab urbancivicny xburbmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
        11,742     0.8654       0.0263        0.81383     0.91706

. drop xburbmi

. 
. * Calculate estimation for pooled Firth model
. * Can take a while to calculate
. mi estimate, cmdok post saving(miest, replace) eform: firthlogit urbancivicny lnpopl c.gdppcthl##c.gdppcthl  c.p
> olityl##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, nolog

Multiple-imputation estimates                   Imputations       =         20
                                                Number of obs     =     11,742
                                                Average RVI       =     0.0032
                                                Largest FMI       =     0.0233
DF adjustment:   Large sample                   DF:     min       =  35,289.59
                                                        avg       =   1.86e+08
                                                        max       =   8.43e+08
Model F test:       Equal FMI                   F(   9, 1.3e+07)  =       9.65
Within VCE type:          OIM                   Prob > F          =     0.0000

---------------------------------------------------------------------------------------
         urbancivicny |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               lnpopl |   1.533431   .1588788     4.13   0.000     1.251617    1.878699
             gdppcthl |   1.758509   .2650802     3.74   0.000      1.30868    2.362958
                      |
c.gdppcthl#c.gdppcthl |   .9712003     .01146    -2.48   0.013     .9489968    .9939233
                      |
              polityl |   .9279909    .026926    -2.58   0.010     .8766894    .9822944
                      |
  c.polityl#c.polityl |   .9857246   .0055146    -2.57   0.010     .9749749    .9965928
                      |
  yrsincleaderinpower |   1.036598   .0139795     2.67   0.008     1.009558    1.064363
          v2x_execorr |   4.951367   3.293104     2.41   0.016      1.34462    18.23268
               lnoill |   .8778079   .0318929    -3.59   0.000     .8174726    .9425963
          postcoldwar |   7.222037   3.024241     4.72   0.000     3.178466    16.40975
                _cons |   5.25e-06   6.46e-06    -9.88   0.000     4.70e-07    .0000586
---------------------------------------------------------------------------------------

. * Predictions of Firth model
. mi predict xburbfirthmi using miest, xb

. generate prurbfirthmi = invlogit(xburbfirthmi)

. * Obtain accuracy of prediction
. sum urbancivicny if e(N_mi)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
urbancivicny |     11,742    .0043434    .0657639          0          1

. local prmean=r(mean)

. * True positive rate
. count if urbancivicny==1 & prurbfirthmi>`prmean' 
  42

. local corposcount=r(N)

. count if urbancivicny==1 & prurbfirthmi<=`prmean' 
  9

. local falseposcount=r(N)

. display (`corposcount')/(`corposcount' + `falseposcount')
.82352941

. * False positive rate
. count if urbancivicny==0 & prurbfirthmi>`prmean' 
  2,884

. local falsenegcount=r(N)

. count if urbancivicny==0 & prurbfirthmi<=`prmean'
  8,807

. local cornegcount=r(N)

. display (`falsenegcount')/(`cornegcount' + `falsenegcount')
.24668548

. macro drop _all

. * Area under the ROC curve
. roctab urbancivicny xburbfirthmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
        11,742     0.8647       0.0267        0.81229     0.91705

. drop xburbfirthmi

. 
. 
. * =====================================================================
. * MODELS FOR TABLE 3.2, FACTORS ASSOCIATED WITH ONSET OF REVOLUTIONARY 
. *   CONTENTION AND ATTEMPTED COUPS--MULTIPLE IMPUTATION MODELS
. * =====================================================================
. * All revolutionary episodes
. mi estimate, post dots eform saving(miest, replace): xtcloglog revny lnpopl gdppcthl gdppcgrow1yrl c.polityl##c.
> polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =     11,742

Group variable: cowcode                         Number of groups  =        164
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
Integration points = 12                                       avg =       71.6
                                                              max =        115
                                                Average RVI       =     0.0118
                                                Largest FMI       =     0.0339
DF adjustment:   Large sample                   DF:     min       =  16,619.31
                                                        avg       = 2795968.29
                                                        max       =   2.66e+07
Model F test:       Equal FMI                   F(   9,930480.6)  =      19.55
Within VCE type:       Robust                   Prob > F          =     0.0000

                                    (Within VCE adjusted for 164 clusters in cowcode)
-------------------------------------------------------------------------------------
              revny |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |    1.41875   .0679445     7.30   0.000      1.29164    1.558369
           gdppcthl |   .9588001    .027525    -1.47   0.143      .906341    1.014295
      gdppcgrow1yrl |   .9746153   .0081375    -3.08   0.002     .9587958    .9906958
            polityl |   .9807792   .0148852    -1.28   0.201     .9520324    1.010394
                    |
c.polityl#c.polityl |   .9899276   .0027066    -3.70   0.000     .9846367     .995247
                    |
yrsincleaderinpower |   1.008977   .0069685     1.29   0.196     .9954111    1.022728
        v2x_execorr |   2.217746   .6494682     2.72   0.007     1.249203    3.937231
             lnoill |   .9909602    .018214    -0.49   0.621     .9558965     1.02731
        postcoldwar |    1.00915   .1549037     0.06   0.953     .7469589    1.363372
              _cons |   .0009952   .0004936   -13.94   0.000     .0003765    .0026308
--------------------+----------------------------------------------------------------
           /lnsig2u |  -2.365383   .6091618                     -3.559318   -1.171448
--------------------+----------------------------------------------------------------
            sigma_u |   .3064528   .0933397                      .1686956    .5567027
                rho |   .0540089   .0311233                      .0170063    .1585378
-------------------------------------------------------------------------------------

. *
. * All rural revolutionary episodes
. mi estimate, post dots eform saving(miest, replace): xtcloglog ruralrevny lnpopl gdppcthl gdppcgrow1yrl c.polity
> l##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =     11,742

Group variable: cowcode                         Number of groups  =        164
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
Integration points = 12                                       avg =       71.6
                                                              max =        115
                                                Average RVI       =     0.0257
                                                Largest FMI       =     0.0490
DF adjustment:   Large sample                   DF:     min       =   7,996.89
                                                        avg       = 3464401.46
                                                        max       =   3.64e+07
Model F test:       Equal FMI                   F(   9,191119.5)  =      12.04
Within VCE type:       Robust                   Prob > F          =     0.0000

                                    (Within VCE adjusted for 164 clusters in cowcode)
-------------------------------------------------------------------------------------
         ruralrevny |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.452477   .1022032     5.30   0.000      1.26536    1.667264
           gdppcthl |   .6942727   .0685931    -3.69   0.000     .5720423    .8426204
      gdppcgrow1yrl |   .9644883   .0095699    -3.64   0.000     .9459121    .9834294
            polityl |   1.019525   .0211021     0.93   0.350     .9789876    1.061742
                    |
c.polityl#c.polityl |   .9976456   .0036236    -0.65   0.516     .9905682    1.004774
                    |
yrsincleaderinpower |   .9828718    .018202    -0.93   0.351     .9478363    1.019202
        v2x_execorr |   2.637673    1.14696     2.23   0.026      1.12469    6.185987
             lnoill |   1.021044   .0272879     0.78   0.436     .9689326    1.075958
        postcoldwar |   .7652157   .1534874    -1.33   0.182     .5164734    1.133757
              _cons |   .0004985   .0003848    -9.85   0.000     .0001098    .0022633
--------------------+----------------------------------------------------------------
           /lnsig2u |  -1.261198   .5267648                     -2.293638    -.228758
--------------------+----------------------------------------------------------------
            sigma_u |   .5322729   .1401913                      .3176456    .8919198
                rho |   .1469284   .0660249                       .057794    .3259724
-------------------------------------------------------------------------------------

. *
. * All urban revolutionary episodes
. mi estimate, post dots eform saving(miest, replace): xtcloglog urbanrevny lnpopl gdppcthl gdppcgrow1yrl c.polity
> l##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =     11,742

Group variable: cowcode                         Number of groups  =        164
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
Integration points = 12                                       avg =       71.6
                                                              max =        115
                                                Average RVI       =     0.0112
                                                Largest FMI       =     0.0470
DF adjustment:   Large sample                   DF:     min       =   8,700.48
                                                        avg       =   1.44e+07
                                                        max       =   1.35e+08
Model F test:       Equal FMI                   F(   9, 1.0e+06)  =       9.79
Within VCE type:       Robust                   Prob > F          =     0.0000

                                    (Within VCE adjusted for 164 clusters in cowcode)
-------------------------------------------------------------------------------------
         urbanrevny |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.355275    .085075     4.84   0.000      1.19838     1.53271
           gdppcthl |   1.043003    .026922     1.63   0.103     .9915482    1.097127
      gdppcgrow1yrl |   .9886568   .0125857    -0.90   0.370     .9642944    1.013635
            polityl |   .9511996   .0194376    -2.45   0.014      .913853    .9900725
                    |
c.polityl#c.polityl |    .984001   .0039346    -4.03   0.000     .9763184    .9917441
                    |
yrsincleaderinpower |   1.021418   .0089742     2.41   0.016     1.003979    1.039159
        v2x_execorr |   1.753255   .7768392     1.27   0.205     .7356835    4.178296
             lnoill |   .9830466   .0256202    -0.66   0.512     .9340927    1.034566
        postcoldwar |   1.311917   .3047612     1.17   0.243     .8320905    2.068438
              _cons |   .0006936   .0004841   -10.42   0.000     .0001766    .0027238
--------------------+----------------------------------------------------------------
           /lnsig2u |  -1.450257   .6494218                     -2.723101   -.1774137
--------------------+----------------------------------------------------------------
            sigma_u |   .4842623   .1572453                      .2562632    .9151138
                rho |   .1247762   .0709215                      .0383904    .3373527
-------------------------------------------------------------------------------------

. *
. * Social revolutionary episodes
. mi estimate, post dots eform saving(miest, replace): xtcloglog leftistny lnpopl gdppcthl gdppcgrow1yrl c.polityl
> ##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =     11,742

Group variable: cowcode                         Number of groups  =        164
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
Integration points = 12                                       avg =       71.6
                                                              max =        115
                                                Average RVI       =     0.0141
                                                Largest FMI       =     0.0567
DF adjustment:   Large sample                   DF:     min       =   5,983.34
                                                        avg       = 2019356.42
                                                        max       =   1.00e+07
Model F test:       Equal FMI                   F(   9,599998.6)  =       9.76
Within VCE type:       Robust                   Prob > F          =     0.0000

                                    (Within VCE adjusted for 164 clusters in cowcode)
-------------------------------------------------------------------------------------
          leftistny |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.427661    .127243     3.99   0.000     1.198837    1.700162
           gdppcthl |   .9095366   .0503882    -1.71   0.087     .8159498    1.013857
      gdppcgrow1yrl |   .9453875    .012149    -4.37   0.000     .9218733    .9695016
            polityl |    1.04359     .02494     1.79   0.074     .9958258    1.093644
                    |
c.polityl#c.polityl |   .9939982   .0049216    -1.22   0.224     .9843979    1.003692
                    |
yrsincleaderinpower |   1.008496   .0153269     0.56   0.578     .9788987    1.038988
        v2x_execorr |    1.87141   .9170753     1.28   0.201     .7162053    4.889906
             lnoill |   1.029679   .0426706     0.71   0.480     .9493521    1.116804
        postcoldwar |   .0775433   .0395477    -5.01   0.000     .0285381    .2106995
              _cons |    .000373   .0003441    -8.56   0.000     .0000612     .002275
--------------------+----------------------------------------------------------------
           /lnsig2u |  -2.307631   1.687231                     -5.614543    .9992814
--------------------+----------------------------------------------------------------
            sigma_u |    .315431   .2661024                      .0603695    1.648129
                rho |   .0570368   .0907453                      .0022107    .6228308
-------------------------------------------------------------------------------------

. *
. * Urban civic revolutionary episodes
. mi estimate, post dots eform saving(miest, replace): xtcloglog urbancivicny lnpopl gdppcthl gdppcgrow1yrl c.poli
> tyl##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =     11,742

Group variable: cowcode                         Number of groups  =        164
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
Integration points = 12                                       avg =       71.6
                                                              max =        115
                                                Average RVI       =     0.0043
                                                Largest FMI       =     0.0243
DF adjustment:   Large sample                   DF:     min       =  32,288.52
                                                        avg       =   2.34e+08
                                                        max       =   1.94e+09
Model F test:       Equal FMI                   F(   9, 6.1e+06)  =       8.49
Within VCE type:       Robust                   Prob > F          =     0.0000

                                    (Within VCE adjusted for 164 clusters in cowcode)
-------------------------------------------------------------------------------------
       urbancivicny |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.472027   .1498249     3.80   0.000     1.205811    1.797017
           gdppcthl |   1.093999   .0412199     2.38   0.017     1.016121    1.177847
      gdppcgrow1yrl |   1.026131   .0121351     2.18   0.029     1.002621    1.050194
            polityl |   .9184554   .0349744    -2.23   0.025     .8524023    .9896269
                    |
c.polityl#c.polityl |   .9852594   .0060937    -2.40   0.016     .9733876    .9972761
                    |
yrsincleaderinpower |   1.035597   .0130664     2.77   0.006     1.010301    1.061526
        v2x_execorr |   4.313372   3.625638     1.74   0.082     .8304868    22.40273
             lnoill |   .9238442   .0301399    -2.43   0.015       .86662    .9848469
        postcoldwar |   8.536188   3.722713     4.92   0.000     3.631172    20.06694
              _cons |   .0000126   .0000177    -8.03   0.000     8.01e-07    .0001976
--------------------+----------------------------------------------------------------
           /lnsig2u |  -1.567387    1.82463                     -5.143595    2.008822
--------------------+----------------------------------------------------------------
            sigma_u |   .4567161   .4166689                      .0763981    2.730298
                rho |   .1125368   .1822299                      .0035357    .8192274
-------------------------------------------------------------------------------------

. *
. * All other revolutionary episodes
. mi estimate, post dots eform saving(miest, replace): xtcloglog noturbancivorleftny lnpopl gdppcthl gdppcgrow1yrl
>  c.polityl##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =     11,742

Group variable: cowcode                         Number of groups  =        164
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
Integration points = 12                                       avg =       71.6
                                                              max =        115
                                                Average RVI       =     0.0214
                                                Largest FMI       =     0.0556
DF adjustment:   Large sample                   DF:     min       =   6,210.16
                                                        avg       = 3708709.56
                                                        max       =   3.82e+07
Model F test:       Equal FMI                   F(   9,262620.0)  =      11.97
Within VCE type:       Robust                   Prob > F          =     0.0000

                                    (Within VCE adjusted for 164 clusters in cowcode)
-------------------------------------------------------------------------------------
noturbancivorleftny |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.384314   .0753306     5.98   0.000     1.244268    1.540121
           gdppcthl |   .8892972   .0466233    -2.24   0.025     .8024512    .9855421
      gdppcgrow1yrl |   .9794272   .0116782    -1.74   0.081     .9568032    1.002586
            polityl |    .977938   .0186171    -1.17   0.241     .9421148    1.015123
                    |
c.polityl#c.polityl |   .9904319   .0037044    -2.57   0.010     .9831966    .9977206
                    |
yrsincleaderinpower |   .9911387   .0133237    -0.66   0.508     .9653657      1.0176
        v2x_execorr |   2.228084   .8015777     2.23   0.026     1.100734    4.510043
             lnoill |   1.013249   .0243634     0.55   0.584     .9666047    1.062145
        postcoldwar |   1.185929   .2084318     0.97   0.332     .8403417    1.673636
              _cons |   .0007615   .0004703   -11.63   0.000     .0002269    .0025551
--------------------+----------------------------------------------------------------
           /lnsig2u |   -2.17936   1.074251                     -4.284853   -.0738675
--------------------+----------------------------------------------------------------
            sigma_u |    .336324   .1806482                      .1173697      .96374
                rho |   .0643406   .0646708                       .008305    .3608751
-------------------------------------------------------------------------------------

. *
. * Coup attempts
. mi estimate, post dots eform saving(miest, replace): xtcloglog coupattempt lnpopl gdppcthl gdppcgrow1yrl c.polit
> yl##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =      8,934

Group variable: cowcode                         Number of groups  =        163
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         21
Integration points = 12                                       avg =       54.8
                                                              max =         69
                                                Average RVI       =     0.0086
                                                Largest FMI       =     0.0294
DF adjustment:   Large sample                   DF:     min       =  22,116.32
                                                        avg       = 8843553.77
                                                        max       =   7.30e+07
Model F test:       Equal FMI                   F(   9, 1.6e+06)  =      23.21
Within VCE type:       Robust                   Prob > F          =     0.0000

                                    (Within VCE adjusted for 163 clusters in cowcode)
-------------------------------------------------------------------------------------
        coupattempt |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   .8725133   .0600075    -1.98   0.047     .7624828    .9984217
           gdppcthl |   .8713096   .0438288    -2.74   0.006     .7895054    .9615899
      gdppcgrow1yrl |   .9826157    .009165    -1.88   0.060     .9648159    1.000744
            polityl |   .9650355   .0147685    -2.33   0.020     .9365184     .994421
                    |
c.polityl#c.polityl |   .9882247   .0024692    -4.74   0.000     .9833968    .9930763
                    |
yrsincleaderinpower |   .8435051   .0236793    -6.06   0.000     .7983482    .8912161
        v2x_execorr |   2.542368   .9525412     2.49   0.013     1.219891    5.298537
             lnoill |   1.058831   .0250116     2.42   0.016     1.010924    1.109008
        postcoldwar |   .5077631   .0773648    -4.45   0.000     .3766765    .6844689
              _cons |   .3721834   .2100092    -1.75   0.080     .1231556     1.12476
--------------------+----------------------------------------------------------------
           /lnsig2u |  -.4878929   .2853193                     -1.047108    .0713227
--------------------+----------------------------------------------------------------
            sigma_u |   .7835296   .1117781                      .5924112    1.036305
                rho |   .2717834   .0564696                      .1758373    .3949917
-------------------------------------------------------------------------------------

. *
. * All urban revolutionary episodes, excluding urban civic episodes
. mi estimate, post dots eform saving(miest, replace): xtcloglog urbanrevnocivic lnpopl gdppcthl gdppcgrow1yrl c.p
> olityl##c.polityl yrsincleaderinpower v2x_execorr lnoill postcoldwar if urbancivicny==0, vce(robust)

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Random-effects complementary log-log model      Number of obs     =     11,691

Group variable: cowcode                         Number of groups  =        164
Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         10
Integration points = 12                                       avg =       71.3
                                                              max =        115
                                                Average RVI       =     0.0210
                                                Largest FMI       =     0.0575
DF adjustment:   Large sample                   DF:     min       =   5,810.11
                                                        avg       = 1072022.93
                                                        max       = 4727082.80
Model F test:       Equal FMI                   F(   9,370034.9)  =       6.22
Within VCE type:       Robust                   Prob > F          =     0.0000

                                    (Within VCE adjusted for 164 clusters in cowcode)
-------------------------------------------------------------------------------------
    urbanrevnocivic |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
             lnpopl |   1.295027   .0846001     3.96   0.000      1.13939    1.471924
           gdppcthl |   1.016846   .0316814     0.54   0.592     .9566072    1.080878
      gdppcgrow1yrl |   .9698628   .0148432    -2.00   0.046     .9412025    .9993957
            polityl |    .969272   .0224733    -1.35   0.178     .9262022    1.014345
                    |
c.polityl#c.polityl |   .9846771   .0049749    -3.06   0.002     .9749733    .9944774
                    |
yrsincleaderinpower |   1.009569   .0122066     0.79   0.431     .9859254    1.033779
        v2x_execorr |    1.30678   .6404877     0.55   0.585     .5000391     3.41508
             lnoill |   1.018248   .0318116     0.58   0.563     .9577684    1.082546
        postcoldwar |   .5592719   .1679974    -1.93   0.053     .3104088    1.007655
              _cons |   .0012013   .0007829   -10.32   0.000     .0003349    .0043089
--------------------+----------------------------------------------------------------
           /lnsig2u |  -1.380516   .7552846                     -2.860847    .0998154
--------------------+----------------------------------------------------------------
            sigma_u |   .5014468   .1893675                      .2392076    1.051174
                rho |   .1325939   .0868674                      .0336164    .4018206
-------------------------------------------------------------------------------------

. 
. 
. * ===================================================
. * COUNTRY PREDICTIONS FROM POOLED PENALIZED MODEL OF 
. *   MULTIPLE IMPUTATION SAMPLE, FIGURES 3.6-3.15
. * ===================================================
. * Obtain average for all country-years for independent states
. sum prurbfirthmi

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
prurbfirthmi |     11,742    .0047135    .0101464   2.20e-18   .1465416

. * Figure 3.6--Tunisia
. list year prurbfirthmi if country=="Tunisia" &  year>1959, clean noobs

    year   prurbf~i  
    1960    .000654  
    1961   .0007265  
    1962   .0007931  
    1963   .0008057  
    1964   .0009167  
    1965    .000974  
    1966     .00105  
    1967   .0005065  
    1968   .0004511  
    1969    .000479  
    1970   .0005112  
    1971   .0006443  
    1972   .0006108  
    1973   .0007329  
    1974   .0007479  
    1975   .0007461  
    1976    .000809  
    1977   .0009023  
    1978   .0009431  
    1979   .0010195  
    1980   .0011053  
    1981   .0012192  
    1982   .0015615  
    1983   .0015896  
    1984   .0017068  
    1985   .0130353  
    1986   .0141961  
    1987   .0048859  
    1988   .0081131  
    1989    .009191  
    1990   .0097803  
    1991   .0110864  
    1992   .0116682  
    1993   .0130745  
    1994   .0150243  
    1995   .0160919  
    1996   .0170306  
    1997   .0190612  
    1998   .0210953  
    1999   .0229511  
    2000   .0252458  
    2001   .0276907  
    2002   .0306616  
    2003   .0311441  
    2004   .0347425  
    2005   .0381816  
    2006   .0411246  
    2007   .0454739  
    2008   .0482864  
    2009   .0529161  
    2010    .056976  
    2011    .025591  
    2012   .0107318  
    2013   .0096906  
    2014   .0070393  

. * Figure 3.7--Serbia
. list year prurbfirthmi if country=="Yugoslavia" &  year>1959, clean noobs

    year   prurbf~i  
    1960   .0012285  
    1961    .001168  
    1962    .001212  
    1963   .0012471  
    1964   .0014106  
    1965   .0015862  
    1966   .0016545  
    1967   .0017932  
    1968   .0018607  
    1969   .0019601  
    1970   .0022784  
    1971   .0026169  
    1972   .0030991  
    1973    .003303  
    1974   .0035437  
    1975   .0042572  
    1976   .0043238  
    1977   .0046124  
    1978   .0052009  
    1979    .005712  
    1980   .0017366  
    1981   .0022673  
    1982   .0022959  
    1983   .0023065  
    1984   .0023295  
    1985   .0170378  
    1986   .0170183  
    1987   .0178015  
    1988    .017505  
    1989   .0172421  
    1990   .0199977  
    1991   .0284112  
    1992   .0248118  
    1993   .0230095  
    1994   .0155545  
    1995   .0169554  
    1996   .0190659  
    1997   .0223921  
    1998   .0297769  
    1999   .0331736  
    2000   .0334956  
    2001   .0068169  
    2002   .0048149  
    2003   .0095306  
    2004   .0132827  
    2005   .0146579  
    2006   .0144073  
    2007   .0092391  
    2008   .0094503  
    2009   .0102355  
    2010   .0102488  
    2011   .0107462  
    2012   .0041361  
    2013   .0044115  
    2014   .0041464  

. * Figure 3.8--Myanmar
. list year prurbfirthmi if country=="Myanmar" &  year>1959, clean noobs

    year   prurbf~i  
    1960   .0000984  
    1961   .0000939  
    1962   .0000909  
    1963    .000772  
    1964   .0008906  
    1965   .0009344  
    1966   .0009757  
    1967    .000994  
    1968   .0010361  
    1969    .001081  
    1970   .0011284  
    1971   .0011853  
    1972   .0012342  
    1973   .0012733  
    1974   .0013204  
    1975   .0015659  
    1976   .0016345  
    1977    .001705  
    1978   .0017841  
    1979    .001889  
    1980   .0019834  
    1981    .001637  
    1982   .0017551  
    1983   .0018564  
    1984   .0019549  
    1985   .0145971  
    1986   .0154402  
    1987   .0161171  
    1988   .0171395  
    1989    .009271  
    1990   .0095469  
    1991   .0090591  
    1992   .0081249  
    1993   .0087351  
    1994   .0093312  
    1995   .0100138  
    1996   .0111902  
    1997   .0122015  
    1998   .0128893  
    1999   .0134587  
    2000   .0148027  
    2001   .0160911  
    2002   .0175203  
    2003   .0464436  
    2004   .0529139  
    2005    .053054  
    2006    .061315  
    2007   .0712244  
    2008   .0823429  
    2009   .1108481  
    2010   .1263948  
    2011   .0716851  
    2012   .0267942  
    2013   .0307654  
    2014   .0275765  

. * Figure 3.9--Ukraine
. list year prurbfirthmi if country=="Ukraine" &  year>1991, clean noobs

    year   prurbf~i  
    1992    .033858  
    1993    .014649  
    1994   .0139296  
    1995   .0062404  
    1996   .0063942  
    1997   .0060658  
    1998   .0061322  
    1999   .0062864  
    2000   .0060765  
    2001   .0084001  
    2002   .0099832  
    2003   .0327839  
    2004   .0387395  
    2005   .0295132  
    2006   .0327929  
    2007   .0298565  
    2008   .0338148  
    2009   .0369397  
    2010   .0287345  
    2011   .0386676  
    2012   .0151884  
    2013   .0161435  
    2014   .0175193  

. * Figure 3.10--South Korea
. list year prurbfirthmi if country=="Korea, South" &  year>1959, clean noobs

    year   prurbf~i  
    1960   .0043259  
    1961   .0003875  
    1962   .0016474  
    1963   .0020144  
    1964   .0018428  
    1965   .0020038  
    1966   .0021473  
    1967   .0023966  
    1968   .0025992  
    1969   .0029397  
    1970   .0034091  
    1971    .003772  
    1972   .0042332  
    1973   .0043133  
    1974   .0061935  
    1975   .0069697  
    1976   .0076923  
    1977   .0090105  
    1978   .0104316  
    1979   .0063077  
    1980   .0070748  
    1981   .0070449  
    1982   .0108851  
    1983   .0122632  
    1984   .0145084  
    1985   .1087023  
    1986    .120001  
    1987   .1389094  
    1988   .1132471  
    1989   .0530309  
    1990   .0573988  
    1991   .0625424  
    1992   .0664662  
    1993   .0574596  
    1994   .0394689  
    1995    .016338  
    1996   .0146211  
    1997   .0160243  
    1998   .0099241  
    1999   .0057918  
    2000   .0048289  
    2001   .0037328  
    2002   .0031038  
    2003   .0060728  
    2004   .0046658  
    2005   .0034456  
    2006   .0027247  
    2007   .0018647  
    2008   .0010086  
    2009   .0010573  
    2010   .0006555  
    2011   .0002983  
    2012   .0001962  
    2013   .0001385  
    2014   .0001041  

. * Figure 3.11--Czechoslovakia
. list year prurbfirthmi if (country=="Czechoslovakia" | country=="Czech Republic") &  year>1959, clean noobs

    year   prurbf~i  
    1960   .0037934  
    1961    .004255  
    1962   .0045406  
    1963   .0046812  
    1964   .0046902  
    1965   .0050825  
    1966   .0055047  
    1967   .0060109  
    1968   .0043949  
    1969   .0047969  
    1970   .0050795  
    1971   .0051336  
    1972     .00555  
    1973   .0059743  
    1974   .0064813  
    1975   .0070636  
    1976   .0075483  
    1977   .0079927  
    1978   .0086616  
    1979   .0091014  
    1980   .0095605  
    1981   .0090412  
    1982   .0093193  
    1983   .0097875  
    1984   .0102459  
    1985   .1262047  
    1986   .1307385  
    1987   .1361329  
    1988   .1407283  
    1989   .1465416  
    1990    .085888  
    1991     .00932  
    1992   .0090092  
    1993   .0051275  
    1994   .0032444  
    1995   .0033539  
    1996   .0035389  
    1997    .003103  
    1998   .0031773  
    1999   .0032392  
    2000   .0033682  
    2001   .0029621  
    2002   .0026067  
    2003   .0058209  
    2004   .0056068  
    2005   .0055367  
    2006   .0053232  
    2007   .0072124  
    2008   .0067478  
    2009   .0059306  
    2010   .0060421  
    2011   .0059026  
    2012   .0030134  
    2013   .0029087  
    2014   .0030627  

. * Figure 3.12--Indonesia
. list year prurbfirthmi if country=="Indonesia" &  year>1959, clean noobs

    year   prurbf~i  
    1960   .0016555  
    1961   .0017993  
    1962   .0019112  
    1963    .001958  
    1964   .0019901  
    1965   .0020884  
    1966   .0011743  
    1967   .0013142  
    1968   .0012238  
    1969   .0013057  
    1970   .0013951  
    1971   .0014978  
    1972   .0016366  
    1973    .001761  
    1974   .0019274  
    1975   .0020479  
    1976   .0021264  
    1977   .0022858  
    1978   .0024542  
    1979   .0026555  
    1980    .002868  
    1981   .0031525  
    1982   .0034194  
    1983   .0035703  
    1984   .0038112  
    1985   .0289419  
    1986   .0304775  
    1987   .0327197  
    1988   .0352672  
    1989   .0380164  
    1990   .0419617  
    1991     .04653  
    1992   .0498844  
    1993   .0570434  
    1994   .0628546  
    1995   .0696622  
    1996   .0777637  
    1997   .0867607  
    1998   .0938309  
    1999   .0255155  
    2000   .0085733  
    2001   .0070615  
    2002    .007662  
    2003   .0083403  
    2004   .0082521  
    2005   .0052198  
    2006    .005797  
    2007   .0064114  
    2008   .0072063  
    2009   .0079494  
    2010   .0084739  
    2011   .0097002  
    2012   .0108709  
    2013   .0121025  
    2014   .0094148  

. * Figure 3.13--Philippines
. list year prurbfirthmi if country=="Philippines" &  year>1959, clean noobs

    year   prurbf~i  
    1960   .0013393  
    1961   .0012039  
    1962   .0012862  
    1963   .0013665  
    1964   .0014755  
    1965   .0013455  
    1966   .0014667  
    1967   .0015553  
    1968   .0016608  
    1969   .0017693  
    1970   .0031798  
    1971   .0033585  
    1972   .0035945  
    1973   .0042051  
    1974   .0047846  
    1975   .0050507  
    1976   .0054252  
    1977   .0059944  
    1978   .0064478  
    1979   .0069105  
    1980   .0032839  
    1981   .0034331  
    1982   .0050131  
    1983   .0054719  
    1984   .0060636  
    1985   .0408162  
    1986   .0403941  
    1987   .0142455  
    1988   .0032252  
    1989   .0034936  
    1990   .0038592  
    1991   .0040995  
    1992   .0034143  
    1993   .0030155  
    1994   .0030957  
    1995   .0035486  
    1996   .0040315  
    1997   .0044674  
    1998   .0042565  
    1999   .0046437  
    2000   .0048644  
    2001   .0046494  
    2002   .0039185  
    2003   .0090368  
    2004   .0097083  
    2005   .0121976  
    2006   .0131361  
    2007    .014198  
    2008   .0156423  
    2009   .0166482  
    2010    .012335  
    2011   .0108328  
    2012   .0045465  
    2013   .0052506  
    2014    .005682  

. * Figure 3.14--Mexico
. list year prurbfirthmi if country=="Mexico" &  year>1959, clean noobs

    year   prurbf~i  
    1960   .0021211  
    1961   .0023359  
    1962    .002448  
    1963   .0025943  
    1964   .0022992  
    1965   .0026385  
    1966   .0028717  
    1967   .0031213  
    1968   .0033621  
    1969   .0036773  
    1970   .0032116  
    1971   .0035023  
    1972   .0037313  
    1973   .0041851  
    1974   .0047018  
    1975   .0050149  
    1976   .0042869  
    1977   .0045284  
    1978   .0055111  
    1979   .0060389  
    1980   .0067041  
    1981   .0072707  
    1982   .0064497  
    1983   .0057425  
    1984     .00554  
    1985   .0408927  
    1986    .043047  
    1987   .0421944  
    1988   .0354799  
    1989   .0334908  
    1990   .0358734  
    1991   .0388068  
    1992    .041367  
    1993   .0440009  
    1994   .0459191  
    1995   .0169029  
    1996   .0160412  
    1997   .0173284  
    1998   .0123966  
    1999   .0133739  
    2000   .0116469  
    2001   .0057756  
    2002   .0061147  
    2003     .00632  
    2004   .0065251  
    2005    .006979  
    2006   .0059986  
    2007   .0065039  
    2008    .006951  
    2009   .0073348  
    2010      .0072  
    2011   .0078618  
    2012   .0068647  
    2013    .007681  
    2014   .0126374  

. * Figure 3.15--Malaysia
. list year prurbfirthmi if country=="Malaysia" &  year>1959, clean noobs

    year   prurbf~i  
    1960   .0000845  
    1961   .0000547  
    1962    .000052  
    1963   .0000523  
    1964   .0000533  
    1965   .0000557  
    1966   .0000589  
    1967   .0000616  
    1968   .0000629  
    1969   .0000683  
    1970   .0003971  
    1971   .0005485  
    1972   .0003248  
    1973   .0003613  
    1974   .0004253  
    1975   .0004697  
    1976   .0003859  
    1977   .0004194  
    1978   .0004617  
    1979   .0005091  
    1980   .0005512  
    1981   .0005178  
    1982   .0006654  
    1983   .0008663  
    1984    .000961  
    1985   .0075296  
    1986   .0075214  
    1987   .0076152  
    1988   .0082801  
    1989   .0093001  
    1990   .0105266  
    1991    .011992  
    1992   .0154165  
    1993   .0174502  
    1994   .0199905  
    1995   .0225323  
    1996    .031116  
    1997   .0347156  
    1998   .0374975  
    1999   .0358587  
    2000   .0388912  
    2001   .0427409  
    2002   .0442299  
    2003   .0223659  
    2004   .0238621  
    2005    .025439  
    2006   .0269388  
    2007   .0283424  
    2008   .0297943  
    2009    .013692  
    2010   .0145278  
    2011   .0155174  
    2012   .0161662  
    2013   .0162898  
    2014    .013978  

. 
. drop prurbfirthmi prurbmi

. 
. log close
      name:  <unnamed>
       log:  C:\Users\mbeissin\Desktop\Stata files for book\Logfiles\chapter3.log
  log type:  text
 closed on:  25 Jan 2022, 20:39:53
------------------------------------------------------------------------------------------------------------------
